Construction deviation detection system and method based on three-dimensional point cloud construction

Abstract

The invention discloses a construction deviation detection system and method based on three-dimensional point cloud construction, and belongs to the technical field of constructional engineering measurement and detection. The system comprises a measurement module which is used for transmitting and receiving laser beams, ultrasonic waves and microwave signals so as to measure various deviations in the building construction process; the sensor network module is used for monitoring environment and structure states, collecting stress, vibration, temperature and humidity data and transmitting the data through the Internet of Things module; the data processing and analyzing module is used for processing and analyzing the data acquired from the measurement module and the sensor network module, generating a three-dimensional model of the building, automatically matching the measurement data with the drawing size data, and marking a deviation position; and the augmented reality module is used for providing a real-time AR view. According to the invention, more comprehensive and accurate measurement data can be provided by adopting various measurement technologies such as laser, ultrasonic wave and microwave, and the device is suitable for various complex building structures and materials.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering measurement and detection, and in particular to a construction deviation detection system and method based on three-dimensional point cloud construction. Background Art

[0002] With the increasing scale of construction projects and the increasing complexity of building structures, the requirements for accuracy and quality in the construction process are becoming higher and higher. In order to ensure the quality and safety of buildings during construction, deviation detection technology has been widely used. However, the existing construction deviation detection methods and systems have some significant defects and are difficult to fully meet the needs of modern construction projects.

[0003] Existing construction deviation detection systems usually rely on a single measurement technology, which has certain limitations in practical applications and is difficult to fully cover complex building structures and diverse material properties. In addition, the measurement accuracy and stability of a single technology may be limited under certain conditions, thus affecting the reliability of the detection results.

[0004] In terms of detection range, traditional ground measurement equipment can usually only cover the lower floors and some areas of buildings, and it is difficult to detect high-rise buildings and hard-to-reach areas. This coverage limitation leads to the inability to detect and correct deviations in some key locations in a timely manner, affecting the overall construction quality. Summary of the invention

[0005] The purpose of the present invention is to provide a construction deviation detection system and method based on three-dimensional point cloud construction, which solves the problems existing in the existing construction deviation detection technology, such as single measurement technology, limited detection range, and limited measurement accuracy and stability.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A construction deviation detection system based on three-dimensional point cloud construction, comprising:

[0008] A measurement module that transmits and receives laser beams, ultrasonic waves, and microwave signals to measure various deviations during construction;

[0009] A sensor network module that monitors the environment and structural status, collects stress, vibration, temperature, and humidity data, and transmits this data through the IoT module;

[0010] A data processing and analysis module is used to process and analyze the data collected from the measurement module and the sensor network module, generate a three-dimensional model of the building, and automatically match the measurement data with the drawing size data, marking the deviation position;

[0011] Augmented reality module, which is used to provide real-time AR view.

[0012] Preferably, the measurement module comprises:

[0013] A laser transmitter and receiver for transmitting and receiving laser beams;

[0014] Ultrasonic transmitter and receiver, which are used to transmit and receive ultrasonic waves;

[0015] Microwave transmitters and receivers for transmitting and receiving microwaves;

[0016] Drones are used to carry high-resolution cameras and laser scanners.

[0017] Preferably, the sensor network module includes:

[0018] Sensor networks, which are used to monitor the environment and structural status;

[0019] IoT modules, which are used to collect and transmit sensor data.

[0020] Preferably, the sensor network includes stress sensors, vibration sensors and environmental sensors.

[0021] Preferably, the data processing and analysis module includes:

[0022] A data processing unit, which is used to process and analyze the collected data and generate a three-dimensional model;

[0023] Dimension matching module, which is used to analyze the drawing dimension data and automatically match it with the measurement data;

[0024] A deviation display module, which is used to mark the deviation position in red and generate a visual deviation map;

[0025] Data storage and management system, which is used to store historical data and real-time data;

[0026] Edge computing units for rapid data processing and analysis on site.

[0027] A construction deviation detection method based on three-dimensional point cloud construction includes the following steps:

[0028] S1. Data collection stage: Through laser, ultrasonic, microwave and drone equipment, comprehensive measurement data of key points in the construction process are collected;

[0029] S2, sensor data collection: monitor the environment and structural status through sensor networks, collect and transmit data in real time;

[0030] S3, data processing and analysis: process and analyze the collected data, generate a three-dimensional model, match it with the design drawings, and mark the deviation position;

[0031] S4, Data storage and management: Store and manage all measurement and analysis data, and use edge computing to improve data processing speed and real-time performance;

[0032] S5. Augmented reality display: The augmented reality module provides real-time AR view to help construction workers visualize deviation locations and adjustment suggestions on site;

[0033] S6. Result verification and adjustment: Make on-site adjustments and corrections based on the AR view and deviation diagram, and re-measure and verify the results;

[0034] S7. Construction progress and material stress monitoring: Real-time monitoring of construction progress and material stress status.

[0035] Preferably, the S1 specifically comprises the following steps:

[0036] S1.1, start the measurement module and obtain accurate measurement data through the laser transmitter and receiver;

[0037] S1.2: Measure structural thickness and material properties using ultrasonic transmitters and receivers;

[0038] S1.3: Obtain detailed information on the internal structure through microwave transmitters and receivers;

[0039] S1.4: Use drones for panoramic scanning and aerial measurements to supplement ground-based measurement data.

[0040] Preferably, the S2 specifically comprises the following steps:

[0041] S2.1, start the sensor network to monitor temperature, humidity, stress and vibration parameters;

[0042] S2.2. Use the Internet of Things module to transmit sensor data to the data processing and analysis module.

[0043] Preferably, the S3 specifically includes the following steps:

[0044] S3.1, generating a three-dimensional model of the building in a data processing unit;

[0045] S3.2, matching the three-dimensional model with the design drawing in the size matching module;

[0046] S3.3. Mark the deviation position in red in the deviation display module and generate a visual deviation map.

[0047] Preferably, the S7 specifically includes the following steps:

[0048] S7.1. Use progress sensors and stress sensors in the sensor network to monitor construction progress and material stress status;

[0049] S7.2. Transmit the monitoring data to the data processing unit for real-time analysis.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention adopts multiple measurement technologies such as laser, ultrasonic and microwave. Compared with the existing technology that only relies on a single measurement method, it can provide more comprehensive and accurate measurement data and is suitable for various complex building structures and materials. The drone carries a high-resolution camera and a laser scanner for aerial inspection, which can cover hard-to-reach areas and provide a full range of building inspection data. The existing technology is mostly ground equipment with limited coverage.

[0052] 2. The present invention utilizes the intersection phenomenon that is bound to occur during the emission of laser, ultrasonic wave and microwave due to the deviation of building dimensions, so that the system can obtain deviation results even for relatively subtle deviations.

[0053] 3. The present invention generates a three-dimensional model of the building, automatically matches it with the design drawings, marks the deviation positions, and uses edge computing units to perform rapid data processing and analysis on site, thereby reducing data transmission delays and improving real-time performance. Compared with traditional cloud computing, the present invention is more suitable for on-site applications that require rapid response.

[0054] 4. The present invention provides a real-time AR view through an augmented reality module to help construction workers intuitively view the deviation position and adjustment suggestions on site, while the prior art usually only provides two-dimensional drawings or simple three-dimensional models and lacks on-site visualization support. Based on the AR view and deviation map, construction workers can make on-site adjustments and corrections, and ensure that all deviations are effectively corrected by re-measuring and verifying the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a system framework diagram of the present invention;

[0056] Figure 2 It is a frame diagram of the measurement module of the present invention;

[0057] Figure 3 It is a sensor network module framework diagram of the present invention;

[0058] Figure 4 It is a data processing and analysis module framework diagram of the present invention;

[0059] Figure 5 is a system flow chart of the present invention;

[0060] Figure 6 is a flow chart of the method of the present invention;

[0061] Figure 7 It is a schematic diagram of the non-biased measurement of the present invention;

[0062] Figure 8 Schematic diagram of deviation measurement of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0064] See also Figure 1-8 The embodiment of the present invention provides a construction deviation detection system based on three-dimensional cloud points, including:

[0065] A measurement module that transmits and receives laser beams, ultrasonic waves, and microwave signals to measure various deviations during construction;

[0066] A sensor network module that monitors the environment and structural status, collects stress, vibration, temperature, and humidity data, and transmits this data through the IoT module;

[0067] A data processing and analysis module is used to process and analyze the data collected from the measurement module and the sensor network module, generate a three-dimensional model of the building, and automatically match the measurement data with the drawing size data, marking the deviation position;

[0068] Augmented reality module, which is used to provide real-time AR view.

[0069] Specifically, the main function of the measurement module is to measure various deviations in the construction process by emitting and receiving laser beams, ultrasonic and microwave signals. It combines a variety of measurement technologies to ensure accurate measurement data under different materials and environmental conditions. It is mainly used to obtain accurate data such as distance, angle, thickness and internal structure in construction. It uses the emission and reception of laser beams, ultrasonic waves and microwaves to reflect and penetrate the target surface or interior. By calculating the reflection time and signal changes, and whether the two line segments will intersect, the various parameters of the building structure and whether there will be deviation problems in the end are determined. By combining laser, ultrasonic and microwave technologies, the measurement module can provide comprehensive measurement data, which is suitable for various complex building structures and materials, and helps to accurately detect various deviations in the construction process.

[0070] The sensor network module is used to monitor the environment and structural status, collect data such as stress, vibration, temperature and humidity in real time, and transmit these data through the Internet of Things module to monitor the environment and stress status of the building structure in real time and obtain comprehensive construction site data. It is mainly achieved by installing sensors at key locations of the building, continuously monitoring and wirelessly transmitting data to the data processing system through the Internet of Things module. The sensor network module provides real-time monitoring of the environment and structural status, ensuring that potential problems are discovered and handled in a timely manner, improving the safety and quality of construction.

[0071] The data processing and analysis module processes and analyzes the data collected from the measurement module and the sensor network module, generates a three-dimensional model of the building, and automatically matches the measurement data with the drawing size data, marks the deviation position, and comprehensively processes multi-source data to generate a three-dimensional building model and identify deviations. Using data processing algorithms and size matching algorithms, the measurement data is compared with the design drawings to generate a visual deviation map. The data processing and analysis module uses efficient data processing and matching algorithms to accurately generate a three-dimensional model and mark the deviation position, thereby improving the efficiency and accuracy of detection.

[0072] The augmented reality module is used to provide real-time AR views to help construction workers visualize deviation locations and adjustment suggestions on site. Through AR technology, the 3D model and the actual construction scene are combined to provide an intuitive view. The virtual model is superimposed on the actual scene using AR equipment to display the deviation location and adjustment information in real time. The augmented reality module enables construction workers to intuitively view and understand the deviation location, make adjustments and corrections in real time, improve construction quality and accuracy, and improve the efficiency of on-site management and decision-making.

[0073] Please see attached Figure 2 , the measurement module includes:

[0074] A laser transmitter and receiver for transmitting and receiving laser beams;

[0075] Ultrasonic transmitter and receiver, which are used to transmit and receive ultrasonic waves;

[0076] Microwave transmitters and receivers for transmitting and receiving microwaves;

[0077] Drones are used to carry high-resolution cameras and laser scanners.

[0078] Specifically, the laser transmitter emits a high-precision laser beam. When the laser beam hits the target surface, it is reflected back to the receiver. The receiver accurately measures the distance between the target and the transmitter by calculating the time difference between the emission and reception of the laser beam. It is used to measure parameters such as the distance, angle and shape of a building, and is particularly suitable for high-precision measurement of exterior walls and the ground.

[0079] The ultrasonic transmitter emits an ultrasonic signal, which propagates inside the building material and reflects back to the receiver. By measuring the time and intensity of the reflected wave, the thickness and internal structure of the material can be analyzed. It is used to measure the thickness of building materials and detect defects in internal structures, such as the thickness of concrete walls and the location of steel bars.

[0080] The microwave transmitter emits microwave signals, which have strong penetrating power and can pass through multiple layers of walls. The receiver receives the penetrated signal and obtains information about the internal structure by analyzing the changes in the signal. It is used to penetrate multiple layers of walls and detect internal structures. It is suitable for detecting complex structures and details hidden under the surface.

[0081] Drones carry high-resolution cameras and laser scanners, and can perform panoramic scans and high-precision measurements of buildings in the air. Drones fly to cover every corner of the building, with cameras taking images and laser scanners measuring distances. They are used to cover high altitudes and hard-to-reach areas that ground equipment cannot reach, providing a full range of building inspection data, especially for high-rise buildings and large-span structures.

[0082] Please see attached Figure 3 , the sensor network module includes:

[0083] Sensor networks, which are used to monitor the environment and structural status;

[0084] IoT modules, which are used to collect and transmit sensor data.

[0085] The sensor network includes stress sensors, vibration sensors, and environmental sensors.

[0086] Specifically, stress sensors are installed at key locations of building structures to measure stress changes in these locations. When a building structure is subjected to loads or other external forces, the stress sensors convert stress changes into electrical signals and transmit these signals to a data processing system. The stress state of the building structure is monitored, potential problems and hidden dangers in the structure are identified, and the safety and stability of the building is ensured.

[0087] Vibration sensors are installed in building structures to detect vibration frequency and amplitude. Vibration sensors can capture tiny vibrations in the structure and convert these vibration information into electrical signals and transmit them to the data processing system. Monitoring the vibration of the building and evaluating the dynamic response and health of the structure are particularly suitable for detecting the response of the building when it is subjected to external shock or vibration.

[0088] Environmental sensors are used to monitor environmental parameters at the construction site, such as temperature, humidity, and air pressure. Environmental sensors sense changes in the surrounding environment, convert these physical quantities into electrical signals, and transmit them to the data processing system. Providing environmental data at the construction site helps construction personnel understand the impact of environmental conditions on construction and structure, and ensures that construction is carried out in a suitable environment.

[0089] The IoT module is responsible for collecting data from various sensors and transmitting the data to the data processing and analysis module through wireless networks. The IoT module uses the Internet protocol to achieve real-time data communication between sensors and the central system, ensuring real-time transmission of sensor data and synchronous update of the central processing system, so that the data processing and analysis module can obtain and process the latest monitoring data in a timely manner.

[0090] The sensor network can monitor the stress, vibration and environmental parameters of the building in real time, providing continuous monitoring data, which helps to detect and deal with potential problems in a timely manner. By integrating stress, vibration and environmental sensors, the sensor network module can provide comprehensive construction site data, covering structural status and environmental conditions. The IoT module realizes the remote transmission of sensor data, so that data processing and analysis can be carried out in real time in the central system, improving the efficiency and real-time nature of data processing. The comprehensive use of stress and vibration sensor data can accurately assess the health of the building, predict and prevent potential structural problems, and ensure construction quality and safety.

[0091] Please see attached Figure 4 , data processing and analysis modules include:

[0092] A data processing unit, which is used to process and analyze the collected data and generate a three-dimensional model;

[0093] Dimension matching module, which is used to analyze the drawing dimension data and automatically match it with the measurement data;

[0094] A deviation display module, which is used to mark the deviation position in red and generate a visual deviation map;

[0095] Data storage and management system, which is used to store historical data and real-time data;

[0096] Edge computing units for rapid data processing and analysis on site.

[0097] Specifically, the specific implementation steps of the above data processing and analysis module are as follows:

[0098] 1. Data processing and 3D model generation:

[0099] In the data processing unit, the collected multi-source data are processed and fused to generate a three-dimensional model of the building. The specific steps are as follows:

[0100] (1) Data preprocessing:

[0101] Data cleaning and denoising: Remove noise and outliers from measurement data.

[0102] Coordinate conversion and alignment: Convert data collected by different devices into a unified coordinate system and align them.

[0103] Data fusion: Data fusion algorithms are used to integrate data from different sources to form a complete 3D point cloud data set.

[0104] 3D modeling: Generate a 3D model of a building from point cloud data. Commonly used algorithms include Poisson surface reconstruction or Delaunay triangulation.

[0105] The formula is as follows:

[0106]

[0107] Among them, P is the point cloud data, w i is the weight, P i It is the point of each data source.

[0108] 2. Size matching and deviation calculation

[0109] In the size matching module, the generated 3D model is automatically matched with the 3D building model in the design drawing.

[0110] Automatically generate 3D models of design drawings: Use the interface of CAD software or other design software to automatically read the dimensional data in the design drawings and generate 3D building models.

[0111] Feature point extraction: Extract key feature points from 3D measurement models and 3D design models.

[0112] Registration algorithm: It uses an iterative closest point algorithm to align the 3D measurement model with the 3D design model.

[0113] The formula is as follows:

[0114]

[0115] Among them, (x1, y1, z1) are the coordinates of the actual measurement point, (x2, y2, z2) are the coordinates of the design point, and δ is the deviation value.

[0116] 3. Deviation display and visualization

[0117] In the deviation display module, the calculated deviation positions are marked graphically in the 3D model.

[0118] Deviation Marking: The locations where the deviation exceeds the set threshold are marked in red and highlighted in the 3D model.

[0119] Visualization diagram generation: Generate a visual deviation diagram to display the deviation location and value for reference by construction personnel.

[0120] 4. Data storage and management

[0121] In the data storage and management system, all historical data and real-time data are stored.

[0122] Data storage: All measurement and analysis data are stored in a database.

[0123] Data management: Provides data query, analysis, and backup functions to ensure that data can be quickly accessed and restored when needed.

[0124] 5. Edge computing and fast on-site processing

[0125] Leverage edge computing units to perform rapid data processing and analysis on-site.

[0126] Edge computing deployment: Deploy edge computing devices at the construction site.

[0127] Real-time data processing: The edge computing unit processes measurement and sensor data in real time for preliminary analysis and model generation.

[0128] Reduced latency: By processing data on-site, the delay in transmitting data to the central server is reduced, improving response speed.

[0129] The formula is as follows:

[0130] T total =T date_processing +T date_transfer +T analysis , where T total is the total processing time, T date_processing is the data processing time, T date_transfer is the data transmission time, T analysis For analysis time.

[0131] In summary, this system can collect construction data from a variety of devices, generate a 3D model, automatically match it with the 3D model of the design drawing, calculate and display deviations, store and manage data, and quickly process it through edge computing. In this way, the system ensures efficient and accurate deviation detection, provides real-time visual feedback, assists construction personnel in making adjustments and corrections, and improves construction quality and accuracy.

[0132] Please see attached Figure 6-8 , a construction deviation detection method based on three-dimensional cloud point construction, comprising the following steps:

[0133] S1. Data collection stage: Through laser, ultrasonic, microwave and drone equipment, comprehensive measurement data of key points in the construction process are collected;

[0134] S2, sensor data collection: monitor the environment and structural status through sensor networks, collect and transmit data in real time;

[0135] S3, data processing and analysis: process and analyze the collected data, generate a three-dimensional model, match it with the design drawings, and mark the deviation position;

[0136] S4, Data storage and management: Store and manage all measurement and analysis data, and use edge computing to improve data processing speed and real-time performance;

[0137] S5. Augmented reality display: The augmented reality module provides real-time AR view to help construction workers visualize deviation locations and adjustment suggestions on site;

[0138] S6. Result verification and adjustment: Make on-site adjustments and corrections based on the AR view and deviation diagram, and re-measure and verify the results;

[0139] S7. Construction progress and material stress monitoring: Real-time monitoring of construction progress and material stress status.

[0140] S1 specifically includes the following steps:

[0141] S1.1. Start the measurement module and obtain accurate measurement data through the laser transmitter and receiver. In this step, two laser transmitters are required to work together. One laser transmitter has a built-in correction function, while the other laser transmitter does not have a correction function. The laser transmitter with a built-in correction function always emits a horizontal straight line, while the laser transmitter without a correction function will be affected by various factors such as the slope of the contact surface with the building, and the laser line segment emitted is not necessarily a horizontal line segment, but may be an inclined line segment. As shown in the attached figure, Figure 7 and 8 As shown, attached Figure 7 When the building plane has no deviation, the laser line segments emitted by the two laser emitters are as follows: Figure 8 When the ground dimensions of the building deviate, the laser segments emitted by the two laser transmitters are in a horizontal state, and when the other laser segment is not horizontally emitted, the two segments will definitely intersect, so the receiver at the predetermined location will receive the data of the intersection of the two segments, and the deviation angle value and other information can be obtained through calculation methods such as the Pythagorean theorem. The specific operations for determining the placement of the receiver at the predetermined location are as follows:

[0142] 1. Determine the location of the laser transmitter

[0143] Horizontal laser transmitter (a laser transmitter): fixed to a reference point (for example, a reference position on a building) to ensure that the laser beam it emits is horizontal.

[0144] Non-horizontal laser transmitter (another laser transmitter): placed on the building contact surface that needs to be detected. The position of the transmitter can be adjusted according to the actual situation of the building plane.

[0145] 2. Calculate the intended receiving location

[0146] (1) Theoretical calculation:

[0147] When there is no deviation in the building plane, the two laser beams will be parallel and will never intersect.

[0148] In case of deviations in the building plane, the two laser beams will intersect.

[0149] (2) Calculate the deviation using the Pythagorean theorem:

[0150] The path of the horizontal laser beam is assumed to be the reference horizontal line, and the intersection point of the non-horizontal laser beam and the reference horizontal line is assumed to be the intersection point.

[0151] If the horizontal distance between the horizontal laser emitter and the non-horizontal laser emitter is D, and the vertical distance where the two laser beams intersect is h, the deviation angle θ can be calculated by the following formula:

[0152]

[0153] From this we can get:

[0154] 3. Predetermine the receiver location

[0155] Place the receiver at the theoretical intersection point. The receiver should be placed at the intersection of the horizontal laser beam and the non-horizontal laser beam.

[0156] Use a measuring device (e.g., a tape measure or a laser rangefinder) to accurately measure the distance D between the two transmitters, and place a receiver at the predetermined distance D perpendicular to the direction of the horizontal laser beam.

[0157] Adjust the height of the receiver so that it can detect the intersection point h of the non-horizontal laser beam.

[0158] 4. Practical operation steps

[0159] Step 1: Fix the horizontal laser transmitter to ensure that the laser beam it emits is horizontal;

[0160] Step 2: Place the non-horizontal laser transmitter so that the laser beam it emits covers the building contact surface that needs to be inspected;

[0161] Step 3: Measure the horizontal distance D between the two laser transmitters;

[0162] Step 4: Place the receiver at the predetermined intersection point and adjust its height so that it can detect the intersection point of the laser beams;

[0163] Step 5: Start the laser transmitter and record the intersection point data detected by the receiver.

[0164] 5. Data Analysis

[0165] The data is collected by a receiver which records the position data of the intersection points of the laser beams and transmits these data to a data processing unit.

[0166] Calculate the deviation angle, use the above formula to calculate the deviation angle θ, and analyze the deviation of the building contact surface.

[0167] The system can effectively detect ground deviations through the combined action of two laser transmitters (one horizontal laser transmitter and one non-horizontal laser transmitter) and the precise placement of receivers at predetermined intersections. The data processing unit uses the data collected by the receivers to calculate the deviation angle value through the Pythagorean theorem, thereby achieving accurate judgment and analysis of the deviation of the building contact surface.

[0168] S1.2, using ultrasonic transmitters and receivers to measure structure thickness and material properties;

[0169] S1.3, obtain detailed information of internal structure through microwave transmitter and receiver;

[0170] S1.4. Use drones for panoramic scanning and aerial measurements to supplement ground measurement data.

[0171] S2 specifically includes the following steps:

[0172] S2.1, start the sensor network to monitor temperature, humidity, stress and vibration parameters;

[0173] S2.2. Use the Internet of Things module to transmit sensor data to the data processing and analysis module.

[0174] S3 specifically includes the following steps:

[0175] S3.1, generating a three-dimensional model of the building in a data processing unit;

[0176] S3.2, matching the three-dimensional model with the design drawing in the size matching module;

[0177] S3.3. Mark the deviation position in red in the deviation display module and generate a visual deviation map.

[0178] S7 specifically includes the following steps:

[0179] S7.1. Use progress sensors and stress sensors in the sensor network to monitor construction progress and material stress status;

[0180] S7.2. Transmit the monitoring data to the data processing unit for real-time analysis.

[0181] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A construction deviation detection system based on three-dimensional point cloud, characterized in that: include: A measurement module that transmits and receives laser beams, ultrasonic waves, and microwave signals to measure various deviations during construction; A sensor network module that monitors the environment and structural status, collects stress, vibration, temperature, and humidity data, and transmits this data through the IoT module; A data processing and analysis module is used to process and analyze the data collected from the measurement module and the sensor network module, generate a three-dimensional model of the building, and automatically match the measurement data with the drawing size data, marking the deviation position; Augmented reality module, which is used to provide real-time AR view.

2. The construction deviation detection system based on three-dimensional point cloud construction according to claim 1, characterized in that: The measuring module comprises: A laser transmitter and receiver for transmitting and receiving laser beams; Ultrasonic transmitter and receiver, which are used to transmit and receive ultrasonic waves; Microwave transmitters and receivers for transmitting and receiving microwaves; Drones are used to carry high-resolution cameras and laser scanners.

3. The construction deviation detection system based on three-dimensional point cloud construction according to claim 1 is characterized in that: The sensor network module comprises: Sensor networks, which are used to monitor the environment and structural status; IoT modules, which are used to collect and transmit sensor data.

4. The construction deviation detection system based on three-dimensional point cloud construction according to claim 1 is characterized in that: The sensor network includes stress sensors, vibration sensors and environmental sensors.

5. The construction deviation detection system based on three-dimensional point cloud construction according to claim 1 is characterized in that: The data processing and analysis module includes: A data processing unit, which is used to process and analyze the collected data and generate a three-dimensional model; Dimension matching module, which is used to analyze the drawing dimension data and automatically match it with the measurement data; A deviation display module, which is used to mark the deviation position in red and generate a visual deviation map; Data storage and management system, which is used to store historical data and real-time data; Edge computing units for rapid data processing and analysis on site.

6. A construction deviation detection method based on three-dimensional point cloud construction, characterized in that: A construction deviation detection system based on three-dimensional point cloud construction according to any one of claims 1 to 5 comprises the following steps: S1. Data collection stage: Through laser, ultrasonic, microwave and drone equipment, comprehensive measurement data of key points in the construction process are collected; S2, sensor data collection: monitor the environment and structural status through sensor networks, collect and transmit data in real time; S3, data processing and analysis: process and analyze the collected data, generate a three-dimensional model, match it with the design drawings, and mark the deviation position; S4, Data storage and management: Store and manage all measurement and analysis data, and use edge computing to improve data processing speed and real-time performance; S5. Augmented reality display: The augmented reality module provides real-time AR view to help construction workers visualize deviation locations and adjustment suggestions on site; S6. Result verification and adjustment: Make on-site adjustments and corrections based on the AR view and deviation diagram, and re-measure and verify the results; S7. Construction progress and material stress monitoring: Real-time monitoring of construction progress and material stress status.

7. The construction deviation detection method based on three-dimensional point cloud construction according to claim 6 is characterized in that: The S1 specifically includes the following steps: S1.1, start the measurement module and obtain accurate measurement data through the laser transmitter and receiver; S1.2, using ultrasonic transmitters and receivers to measure structure thickness and material properties; S1.3, obtain detailed information of internal structure through microwave transmitter and receiver; S1.

4. Use drones for panoramic scanning and aerial measurements to supplement ground measurement data.

8. The construction deviation detection method based on three-dimensional point cloud construction according to claim 6 is characterized in that: The S2 specifically includes the following steps: S2.1, start the sensor network to monitor temperature, humidity, stress and vibration parameters; S2.

2. Use the Internet of Things module to transmit sensor data to the data processing and analysis module.

9. The construction deviation detection method based on three-dimensional point cloud construction according to claim 6 is characterized in that: The S3 specifically includes the following steps: S3.1, generating a three-dimensional model of the building in a data processing unit; S3.2, matching the three-dimensional model with the design drawing in the size matching module; S3.

3. Mark the deviation position in red in the deviation display module and generate a visual deviation map.

10. The construction deviation detection method based on three-dimensional point cloud construction according to claim 6, characterized in that: The S7 specifically comprises the following steps: S7.

1. Use progress sensors and stress sensors in the sensor network to monitor construction progress and material stress status; S7.

2. Transmit the monitoring data to the data processing unit for real-time analysis.

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