A building measurement method and system based on intelligent robot

Abstract

The present invention provides a building measurement method and system based on an intelligent robot, which relates to the technical field of intelligent robots. The method comprises: deploying an intelligent robot to a building area to be measured; determining the final driving path of the intelligent robot according to the layout of the building area to be measured; the intelligent robot automatically drives along the final driving path, and uses internal sensor components to collect three-dimensional point cloud data, image information and geographic location information of the building environment; processing the three-dimensional point cloud data, image information and geographic location information of the building environment, including point cloud splicing, denoising, registration and surface reconstruction, to generate a three-dimensional model of the building. The present invention efficiently generates a three-dimensional building model and a detailed measurement report by automatically collecting and processing data, thereby improving measurement accuracy and efficiency and reducing labor costs.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent robots, and in particular to a building measurement method and system based on intelligent robots. Background Art

[0002] Traditional manual measurement methods, such as using tape measures and laser rangefinders, are limited by the operator's skill level, environmental factors (such as wind and temperature), and the accuracy of the equipment itself, making it difficult to achieve high-precision measurements, especially in large or complex buildings, where the problem of error accumulation is particularly prominent. Manual measurement needs to be carried out point by point. For large buildings or areas that require detailed measurements, the measurement process is time-consuming and labor-intensive, inefficient, and difficult to ensure the comprehensiveness and continuity of the measurement.

[0003] In some complex or dangerous environments, such as high altitude, narrow space, toxic and hazardous environment, manual measurement has great potential safety hazards, and operators need to bear higher risks. Due to possible operational errors and recording errors in the manual measurement process, the measurement data is inconsistent, which affects the subsequent data processing and analysis results.

[0004] In addition, it relies on manual operation and lacks intelligent and automated capabilities, making it impossible to achieve real-time processing, analysis and feedback of measurement data, which limits the application scope and efficiency of measurement technology. It mainly focuses on the measurement of the geometric dimensions of buildings, but it is difficult to effectively extract the key structural features of buildings, such as the location of beams and columns, wall types, and the location of doors and windows. It lacks the ability to analyze spatial layout, making it impossible to reasonably divide and evaluate building space, making it difficult to meet the needs of building design, planning and use. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a building measurement method and system based on an intelligent robot, which realizes the automation, intelligence and efficiency of building measurement.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a building measurement method based on an intelligent robot comprises:

[0008] Deploy the intelligent robot to the building area to be tested;

[0009] Determine the final driving path of the intelligent robot based on the layout of the building area to be tested;

[0010] The intelligent robot automatically drives along the final driving path and uses the internal sensor components to collect 3D point cloud data, image information and geographic location information of the building environment;

[0011] Processing of 3D point cloud data, image information and geographic location information of the building environment, including point cloud stitching, denoising, registration and surface reconstruction, to generate a 3D model of the building;

[0012] According to the 3D model of the building, the basic dimensions, area, and volume measurement parameters of the building are automatically calculated, and the key structural features of the building are extracted to perform spatial layout analysis and structural stability assessment measurement tasks to obtain comprehensive measurement and analysis results;

[0013] Based on the comprehensive measurement and analysis results, a measurement report is automatically generated, including the 3D model of the building, various measurement parameter tables and space analysis reports.

[0014] Furthermore, according to the layout of the building area to be tested, the final driving path of the intelligent robot is determined, including:

[0015] The building area to be tested is taken as a search space to be solved, and the building area to be tested is divided into multiple sub-areas, wherein the multiple sub-areas are used as initial branch nodes;

[0016] Define evaluation criteria for each branch node, and start from the current node and continuously branch out new child nodes;

[0017] According to the evaluation criteria, the evaluation value of each new child node is calculated, and the candidate nodes for constructing the final path are identified by comparing the evaluation values ​​of different child nodes;

[0018] The search space is continuously explored and delimited. When the search space reaches the preset exploration and delimitation conditions, the intelligent robot determines the final driving path from the starting point to the end point based on the identified candidate nodes.

[0019] Furthermore, the calculation formula for the evaluation value of each new child node is: ;

[0020] in, Indicates the evaluation value of the new child node; , , , , , , They represent the weight coefficients of path length, obstacles, feature points, energy consumption, wind direction, temperature, and humidity respectively; represents the path length; Indicates the area of ​​the obstacle; Indicates the total area; Indicates the obstacle type coefficient; Indicates the obstacle type; Indicates the number of feature points; Indicates the priority of feature points; Indicates the feature point type coefficient; Indicates the number of feature point types; An index representing the type of feature point; Indicates The type of feature points; Indicates The number of feature points; represents the terrain coefficient; Indicates load influence coefficient; represents the robot load; Energy consumption coefficient indicating speed; Indicates the current speed of the robot; Indicates the maximum speed of the robot; Indicates the energy consumption coefficient of the load; Energy consumption coefficient indicating external conditions; Indicates wind direction; Indicates temperature; indicates humidity.

[0021] Furthermore, the intelligent robot automatically drives along the final driving path and uses internal sensor components to collect three-dimensional point cloud data, image information and geographic location information of the building environment, including:

[0022] The intelligent robot automatically drives according to the final driving path;

[0023] During driving, the laser radar continuously emits laser beams and receives reflected signals. It calculates the three-dimensional coordinate information of the building environment through the time difference and angle information of the reflected signals to form three-dimensional point cloud data.

[0024] The camera captures image information of the building environment at the same frame rate as the lidar, including the appearance, texture, and color of the building; the positioning system receives satellite signals in real time, calculates and records the geographic location information of the intelligent robot, including longitude, latitude, and altitude.

[0025] Furthermore, the three-dimensional point cloud data, image information and geographic location information of the building environment are processed, including point cloud stitching, denoising, registration and surface reconstruction, to generate a three-dimensional model of the building, including:

[0026] Splice multiple 3D point cloud data collected by LiDAR at different positions and angles, and identify and match the overlapping parts in the point cloud data;

[0027] Treat the points in the point cloud data as individuals and initialize the group composed of each individual;

[0028] Define a fitness function and evaluate each individual to obtain the evaluation result of the fitness function;

[0029] According to the evaluation results of the fitness function, individuals learn and update their own states, and the learning and updating process is repeated until the preset number of iterations is reached to obtain the group learning results;

[0030] According to the group learning results, the points marked as noise are removed to obtain denoised point cloud data;

[0031] The denoised point cloud data is registered with the image information captured by the camera to obtain the registered point cloud data and image information;

[0032] Based on the registered point cloud data and image information, a surface fitting algorithm is used to construct a three-dimensional model containing the building's geometric shape, structure, texture and color information.

[0033] Furthermore, the calculation formula of the fitness function is: ;

[0034] in, Indicate point The fitness value of Represents a point in a 3D point cloud; represents the consistency weight coefficient of the normal vector; Indicates the number of neighborhood points; Indicate point The normal vector of Indicate point Neighborhood points The normal vector of represents the tangent vector consistency weight coefficient; Indicates the number of neighborhood points used to calculate the average of tangent vector differences; Indicate point The tangent vector of Indicate point Neighborhood points The tangent vector of represents the Gaussian curvature weight coefficient; Indicate point Gaussian curvature of represents the mean curvature weight coefficient; Indicate point The number of points in the surrounding neighborhood; Represents an index variable; Indicate point The surrounding neighborhood The curvature of a point; represents the curvature variance weight coefficient; Represents the point density weight coefficient; Indicate point The volume of the surrounding neighborhood.

[0035] Furthermore, based on the 3D model of the building, the basic dimensions, area, and volume measurement parameters of the building are automatically calculated, and the key structural features of the building are extracted to perform spatial layout analysis and structural stability assessment measurement tasks, and obtain comprehensive measurement and analysis results, including:

[0036] Using the three-dimensional model data, the basic dimensions of the building, the area of ​​each surface and the total volume of the building are calculated to obtain the basic dimensions of the building, the area of ​​each surface and the total volume data;

[0037] Analyze the basic dimensions, surface areas and total volume data of the building, identify the key structural features of the building, including the location of beams and columns, wall types, and the location of doors and windows, and extract the geometric information and attribute information of the key structural features from the 3D model of the building;

[0038] The geometric information and attribute information of the extracted key structural features are sorted to form a structural feature data set, and each feature in the structural feature data set is numbered and classified to obtain a numbered and classified key structural feature data set;

[0039] Based on the 3D model of the building, the numbered and classified key structural feature datasets, the building space is divided into different areas, and the purpose and function of each area are determined;

[0040] Analyze the layout of each area, including space size, shape, ventilation, and lighting, to evaluate the rationality of the layout and obtain the spatial layout analysis results;

[0041] Establish a structural analysis model of the building based on the spatial layout analysis results, the three-dimensional model of the building, and the numbered and classified key structural feature data sets;

[0042] Conduct stress analysis on the structural analysis model of the building, simulate the stress of the building under specific loads, including wind load, earthquake, and deadweight, and evaluate the stability of the building under specific loads to obtain a structural stability assessment report;

[0043] The basic dimensions of the building, surface area and total volume data, spatial layout analysis results and structural stability assessment report are summarized to obtain comprehensive measurement and analysis results.

[0044] In a second aspect, a building measurement system based on an intelligent robot comprises:

[0045] Intelligent robotic modules for autonomous navigation, positioning and traversal of the interior and exterior spaces of buildings, and for 3D scanning and data collection;

[0046] The data processing module is used to receive the three-dimensional scanning data collected by the intelligent robot, and perform pre-processing, denoising, and splicing to generate a three-dimensional model of the building;

[0047] The feature extraction module is used to analyze the three-dimensional model of the building, automatically identify and extract key structural features, including the location of beams and columns, wall types, and the location of doors and windows, and extract the geometric information and attribute information of key structural features;

[0048] The spatial layout analysis module is used to divide the building space into different areas according to the 3D model of the building and the extracted key structural features, and determine the purpose and function of each area; analyze the layout of each area, including space size, shape, ventilation, and lighting surface;

[0049] The structural stability assessment module is used to establish a structural analysis model using the building 3D model and key structural feature data, and to perform stress analysis on the structural analysis model, assess the stability of the building under specific loads, and generate a comprehensive measurement and analysis report;

[0050] The user interaction interface module is used to provide a user interaction interface, allowing users to set measurement parameters, view measurement progress, obtain measurement results and analysis reports.

[0051] According to a third aspect, a computing device includes:

[0052] one or more processors;

[0053] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0054] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0055] The above solution of the present invention includes at least the following beneficial effects:

[0056] By using intelligent robots to automatically drive and collect data, human errors in manual measurement are avoided and the measurement accuracy is improved. At the same time, intelligent robots can continuously and quickly collect a large amount of data, which improves the measurement efficiency. Intelligent robots can enter environments that are difficult to reach or dangerous for human beings to measure, ensuring the comprehensiveness of the measurement. At the same time, through continuous data collection, complete three-dimensional information of the building environment can be obtained, avoiding omissions or breakpoints that may occur in traditional measurements. Using intelligent robots for measurement avoids manual work in dangerous environments such as high altitudes and narrow spaces, significantly reducing safety risks during the measurement process.

[0057] Through the automatic data collection of intelligent robots and combined with advanced data processing technology, the measurement is automated and intelligent. From data collection to result analysis, the entire process does not require human intervention, which improves the convenience and intelligence of measurement. Through processing technologies such as point cloud stitching, denoising, registration and surface reconstruction, a high-precision three-dimensional model of the building can be generated. The model intuitively shows the shape and structure of the building. It can not only automatically calculate the basic dimensions, area, volume and other measurement parameters of the building, but also extract the key structural features of the building, perform spatial layout analysis and structural stability assessment. These comprehensive measurement and analysis results provide strong support for building design, construction and maintenance. Based on the comprehensive measurement and analysis results, this method can automatically generate a detailed measurement report, including a three-dimensional model of the building, various measurement parameter tables and a spatial analysis report. This simplifies the process of collating and presenting measurement results and improves work efficiency and report quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a building measurement method based on an intelligent robot provided in an embodiment of the present invention.

[0059] Figure 2 It is a schematic diagram of a building measurement system based on an intelligent robot provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a building measurement method based on an intelligent robot, and the method comprises the following steps:

[0062] Step 11, deploying the intelligent robot to the building area to be tested;

[0063] Step 12, determining the final driving path of the intelligent robot according to the layout of the building area to be tested;

[0064] Step 13, the intelligent robot automatically drives along the final driving path and uses internal sensor components to collect three-dimensional point cloud data, image information and geographic location information of the building environment;

[0065] Step 14, processing the three-dimensional point cloud data, image information and geographic location information of the building environment, including point cloud stitching, denoising, registration and surface reconstruction, to generate a three-dimensional model of the building;

[0066] Step 15, based on the three-dimensional model of the building, automatically calculate the basic dimensions, area, and volume measurement parameters of the building, extract the key structural features of the building, perform spatial layout analysis, and structural stability assessment measurement tasks to obtain comprehensive measurement and analysis results;

[0067] Step 16, based on the comprehensive measurement and analysis results, automatically generate a measurement report, including a three-dimensional model of the building, various measurement parameter tables and a space analysis report.

[0068] In the embodiment of the present invention, by deploying intelligent robots, rapid response and accurate measurement of building areas can be achieved. The intelligent robots have autonomous navigation and positioning capabilities and can accurately enter the area to be measured. This step ensures the comprehensiveness and accuracy of the measurement and avoids the errors and delays that may be caused by manual deployment.

[0069] Step 12: By accurately planning the driving path of the intelligent robot, the measurement efficiency can be maximized and redundant data can be reduced. The path planning takes into account the layout characteristics of the building area, ensuring that the intelligent robot can cover all areas that need to be measured while avoiding repeated measurements and omissions. This helps save measurement time, reduce measurement costs, and improve data integrity and accuracy.

[0070] Step 13: The intelligent robot drives automatically and collects data, realizing the automation and intelligence of measurement. The high precision and stability of the sensor components ensure the quality of the collected data. The comprehensive collection of 3D point cloud data, image information and geographic location information helps to generate more accurate and comprehensive measurement results.

[0071] Step 14: The refinement and specialization of the data processing step ensures that the generated 3D model has high accuracy and realism. The application of point cloud stitching, denoising, registration and surface reconstruction technologies effectively solves the errors and noise problems that may occur during data collection.

[0072] Step 15, based on the automatic calculation and feature extraction of the 3D model, the measurement is made more efficient and accurate. By calculating the basic dimensions, area, volume and other parameters of the building, the geometric information of the building can be quickly obtained. At the same time, the extraction of key structural features and the completion of measurement tasks such as spatial layout analysis and structural stability assessment provide comprehensive data support for building design, construction and maintenance. This helps to discover potential problems and risks and improve the safety and stability of the building.

[0073] Step 16: Automatic generation of measurement reports simplifies the process of collating and presenting measurement results, improving work efficiency and report quality. The report is comprehensive and accurate, including the 3D model of the building, various measurement parameter tables, and spatial analysis reports, providing a convenient reference for building design, construction, and maintenance. This helps save manpower and time costs and improves the efficiency and accuracy of the entire measurement process.

[0074] In a preferred embodiment of the present invention, the above step 11, deploying the intelligent robot to the building area to be tested, may include:

[0075] In the embodiment of the present invention, the specific scope of the building area to be measured, the purpose of measurement, the required accuracy and other requirements are clarified. According to the measurement requirements, a suitable intelligent robot model is selected. Consider the robot's autonomous navigation capability, sensor configuration (such as laser radar, camera, etc.), endurance, protection level and other factors to ensure that the robot can meet the requirements of the measurement task. Based on the factory settings of the robot, the parameters of the robot are configured according to the characteristics of the building area to be measured and the measurement requirements. This includes setting the navigation mode, sensor sampling frequency, data storage method, etc. to ensure that the robot can work stably and accurately during the measurement process.

[0076] Go to the building area to be tested for on-site survey to understand the building's structural layout, passage conditions, obstacle distribution, etc., to provide a practical basis for the deployment and path planning of the robot. Determine the initial deployment position of the robot based on the results of the on-site survey. Select a location that is convenient for the robot to enter and exit, and ensure that the location can cover the main part of the building area to be tested. Use the on-site survey data to draw a map of the building area to be tested. The map should contain information such as the building's outline, passages, obstacles, etc., to provide a basis for the robot's path planning. Plan the robot's driving path on the map. Consider the building's layout characteristics, measurement requirements, and the robot's navigation capabilities to ensure that the path can cover all areas that need to be measured while avoiding collisions and getting lost. Transport the intelligent robot to the site and place it at the predetermined initial deployment position. According to the robot's operating manual, start the robot and ensure that it is in normal working condition. Check whether the robot's sensors, navigation system, communication equipment, etc. are operating normally. Import the planned path into the robot's control system. Ensure that the robot can automatically drive along the predetermined path and collect data. After the robot starts measuring, observe the robot's driving status and data collection in real time through the remote monitoring system. Ensure that the robot follows the planned path and can respond to emergencies in a timely manner.

[0077] If the robot encounters an obstacle or deviates from the predetermined path during driving, the robot's path should be adjusted in time through the remote control system. Ensure that the robot can successfully complete the task and collect accurate data. The robot drives along the predetermined path and uses the internal sensor components to collect 3D point cloud data, image information and geographic location information of the building environment. Ensure the integrity and accuracy of data collection.

[0078] The robot transmits the collected data to the data processing center wirelessly or wiredly to ensure timely transmission and storage of data.

[0079] In a preferred embodiment of the present invention, the above step 12, determining the final driving path of the intelligent robot according to the layout of the building area to be tested, may include:

[0080] Step 121, taking the building area to be tested as a search space to be solved, and dividing the building area to be tested into multiple sub-areas, wherein the multiple sub-areas serve as initial branch nodes;

[0081] Step 122, defining an evaluation criterion for each branch node, and continuously branching out new child nodes from the current node;

[0082] Step 123, calculating the evaluation value of each new child node according to the evaluation criteria, and identifying the candidate nodes for constructing the final path by comparing the evaluation values ​​of different child nodes;

[0083] Step 124, continue to explore and delimit the search space. When the search space reaches the preset exploration and delimitation conditions, the intelligent robot determines the final driving path from the starting point to the end point based on the identified candidate nodes.

[0084] In the embodiment of the present invention, a plan view or a three-dimensional model of the building area to be tested is obtained to have a comprehensive understanding of its structure. According to the layout characteristics of the building area, such as room distribution, corridor direction, staircase location, etc., it is divided into multiple sub-areas. Each sub-area should be as small as possible to plan the path more finely. These sub-areas are regarded as initial branch nodes.

[0085] Step 122, based on the measurement requirements, define evaluation criteria. These criteria may include path length, measurement efficiency, safety, accessibility, etc. Starting from the current node (i.e., the starting point or the determined path point), consider all possible movement directions and branch out a new child node for each direction. Perform a preliminary evaluation on each new child node to determine whether it meets the basic measurement requirements and accessibility requirements.

[0086] Step 123, using the evaluation criteria, conduct a detailed evaluation of each new child node. Calculate its path length, estimated measurement time, collision risk and other indicators. Based on the evaluation results, screen the child nodes and identify those child nodes whose evaluation values ​​meet specific conditions as candidate nodes. Candidate nodes should be nodes that form the final path from the current node in the current search space.

[0087] Step 124, repeat steps 122 and 123 continuously to explore and delimit the search space in depth. Each iteration is based on the results of the previous one to further narrow the search scope. When the search space reaches the preset exploration and delimitation conditions (such as reaching the maximum number of iterations, finding a path that meets the requirements, etc.), stop searching. Based on the identified candidate nodes, according to the evaluation value specific rules (such as the shortest path, the highest efficiency, etc.), determine the final driving path from the starting point to the end point.

[0088] Suppose there is an office floor with multiple rooms and corridors that needs to be measured. First, the floor plan of the floor is obtained and divided into multiple sub-areas (such as Room A, Corridor B, Stairwell C, etc.) according to the layout of the rooms, corridors, and stairs. These sub-areas become our initial branching nodes. Define evaluation criteria, including path length, measurement efficiency, and safety. Starting from the starting point (such as the floor entrance), we consider all possible movement directions and branch out new sub-nodes for each direction (such as entering Room A from the entrance, moving along Corridor B, etc.). Each new node is initially evaluated to ensure that it is reachable and meets basic measurement requirements.

[0089] Each new node is evaluated in detail. For example, the path length, estimated measurement time, and collision risk from the starting point to each node are calculated. Then, the nodes with the final evaluation value are selected as candidate nodes. Steps 122 and 123 are repeated continuously to deeply explore and delimit the search space. After many iterations, the final path from the starting point to the end point is found (such as entering room A from the entrance, moving along corridor B to stairwell C, and then going upstairs to the target area). This path not only meets the measurement requirements, but also optimizes the path length and measurement efficiency.

[0090] By dividing the sub-areas and defining the evaluation criteria, all possible paths can be systematically explored and evaluated, thereby improving the efficiency of path planning. The final path selected according to the evaluation criteria can ensure the highest measurement efficiency, the shortest path or the lowest collision risk, thereby optimizing the measurement effect. This method can be applied to building areas of different layouts and complexities. By adjusting the evaluation criteria and search conditions, driving paths that meet different needs can be flexibly planned. The entire path planning process is automatically carried out based on algorithms and evaluation criteria, reducing manual intervention and decision-making, and improving the degree of automation of measurement.

[0091] In a preferred embodiment of the present invention, the calculation formula of the evaluation value of each new child node is: ;

[0092] in, Indicates the evaluation value of the new child node; , , , , , , They represent the weight coefficients of path length, obstacles, feature points, energy consumption, wind direction, temperature, and humidity respectively; represents the path length; Indicates the area of ​​the obstacle; Indicates the total area; Indicates the obstacle type coefficient; Indicates the obstacle type; Indicates the number of feature points; Indicates the priority of feature points; Indicates the feature point type coefficient; Indicates the number of feature point types; An index representing the type of feature point; Indicates The type of feature points; Indicates The number of feature points; represents the terrain coefficient; Indicates load influence coefficient; represents the robot load; Energy consumption coefficient indicating speed; Indicates the current speed of the robot; Indicates the maximum speed of the robot; Indicates the energy consumption coefficient of the load; Energy consumption coefficient indicating external conditions; Indicates wind direction; Indicates temperature; indicates humidity.

[0093] In the embodiment of the present invention, the path length is determined based on the actual distance between the current node and the new child node. This can be obtained through map data, sensor measurements. Obtain the evaluation value of the path length. Determine the obstacle area in the area where the new child node is located through map data or sensor measurement. and total area Determine the obstacle type coefficient based on the type of obstacle (such as walls, furniture, people, etc.) and obstacle type Different types of obstacles may have different effects on the robot's movement. Get the evaluation value of the obstacle part.

[0094] Determine the number of feature points in the area where the new child node is located through map data and the priority of each feature point . According to the type of feature point (such as corners, doorways, landmarks, etc.), determine the feature point type coefficient , and count the number of feature points of each type .pass Get the evaluation value of the feature point part.

[0095] Determine the terrain coefficient of the area where the new child node is located through sensor measurement (such as flat, rugged, etc.).

[0096] Get the current load of the robot ,speed and maximum speed . Determine the energy consumption coefficient of the load according to the load and speed of the robot Energy consumption coefficient of speed These factors reflect the effect of load and speed on energy consumption. Get the current wind direction ,temperature and humidity , and determine the energy consumption coefficient of external conditions These external conditions affect the energy consumption of the robot. The evaluation value of the energy consumption part is obtained. The negative sign here indicates that the energy consumption is a negative contribution to the evaluation value. Add the evaluation values ​​of the path length part, obstacle part, feature point part and energy consumption part to obtain the comprehensive evaluation value of the new child node. .

[0097] The evaluation value calculation formula takes into account multiple factors such as path length, obstacles, feature points, energy consumption, and external conditions (such as wind direction, temperature, and humidity), making path planning more comprehensive and accurate. By updating the weight coefficients and various parameters in real time, the evaluation value can dynamically reflect the current environmental status, thereby adjusting the path planning in a timely manner to ensure that the robot always follows the final path. Path planning that takes multiple factors into consideration can reduce the risk of the robot colliding with obstacles, getting lost, or consuming too much energy, and increase the success rate of task completion.

[0098] The evaluation value takes into account the impact of load and speed on energy consumption, as well as the additional impact of external conditions (such as wind direction and temperature) on energy consumption. By optimizing the path and speed, the robot's energy consumption can be effectively reduced and its endurance can be extended. Adjusting the path and speed according to the load can prevent the robot from running out of power or being damaged prematurely due to excessive load, thereby optimizing resource utilization. Factors such as obstacle type, terrain coefficient, and external conditions in the evaluation value calculation formula enable the robot to better adapt to complex and changing building environments.

[0099] In a preferred embodiment of the present invention, the above step 13, in which the intelligent robot automatically drives along the final driving path and uses the internal sensor components to collect the three-dimensional point cloud data, image information and geographic location information of the building environment, may include:

[0100] Step 131, the intelligent robot automatically drives according to the final driving path;

[0101] Step 132, during the driving process, the laser radar continuously emits laser beams and receives reflected signals, and calculates the three-dimensional coordinate information of the building environment through the time difference and angle information of the reflected signals to form three-dimensional point cloud data;

[0102] In step 133, the camera captures image information of the building environment at the same frame rate as the lidar, including the appearance, texture, and color of the building; the positioning system receives satellite signals in real time, calculates and records the geographic location information of the intelligent robot, including longitude, latitude, and altitude.

[0103] In the embodiment of the present invention, the intelligent robot first loads the pre-planned final driving path, which consists of a series of coordinate points or instructions, and clarifies the robot's driving direction and distance. The robot uses the internal navigation control system to adjust its own driving direction and speed in real time according to the loaded path information to ensure that it moves along the planned path. During the driving process, the robot monitors the surrounding environment in real time through sensors such as laser radar, and can automatically avoid obstacles when encountering obstacles to ensure driving safety.

[0104] In step 132, the laser radar emits laser beams at a preset frequency (e.g., several to dozens of times per second), and these laser beams cover the scanning area in front of the robot. The laser beam will be reflected back after encountering the surface of an object in the building environment, and the laser radar receives these reflected signals. Each reflected signal contains the time difference and angle information from the emission to the reception of the laser beam. The processor records the timestamps of each laser beam emission and reception, and calculates the time difference between them. At the same time, the processor obtains the angle information of the laser radar when emitting the laser beam, including the horizontal scanning angle and (for three-dimensional laser radar) the vertical scanning angle. Using the time difference and the speed of light, the processor calculates the distance from the laser radar to the reflection point. (because the laser beam needs to go back and forth); among them, It is the speed of light; is the time difference between the emission and reception of each laser beam. For each reflected signal, the processor uses the angle information and distance to calculate the three-dimensional coordinates of the reflection point in the robot coordinate system. ;in, is the position coordinate of the laser radar in the robot coordinate system; is the scanning angle of the laser radar in the horizontal direction; is the scanning angle of the LiDAR in the vertical direction.

[0105] Step 133, synchronized with the laser radar, the camera on the intelligent robot captures image information of the building environment at the same frame rate, including the appearance, texture, color, etc. of the building. The captured image information is compressed, encoded, and stored in the robot's internal memory for subsequent analysis and processing. The positioning system (such as GPS) on the intelligent robot receives signals from satellites in real time, and these signals contain the robot's geographic location information. Based on the received satellite signals, the positioning system calculates the robot's longitude, latitude, altitude, and other geographic location information through algorithms, and records this information in the robot's data system.

[0106] Assume that the laser radar emits a laser beam at a certain moment, and the calculated distance meters. Using the angle information and distance, the three-dimensional coordinates of the reflection point are calculated to be (10.61, 10.61, 8.5). Assume that the robot happens to drive to the door of a building at a certain moment, and the camera captures a clear image of the building, including its appearance, the location and color of the doors and windows. At the same time, the positioning system records the robot's geographic location information, such as longitude 120.0000∘, latitude 30.0000∘ and altitude 10 meters (assuming that the robot is 10 meters above the ground).

[0107] Intelligent robots can drive automatically and collect 3D point cloud data, image information and geographic location information at the same time, which improves the efficiency of data collection. The synchronous operation of laser radar and camera, as well as the accurate recording of positioning system, ensure the high quality and accuracy of the collected data. The application of intelligent robots reduces the dependence on manual labor and reduces the labor cost of data collection. Intelligent robots can automatically avoid obstacles and adapt to complex environments, making data collection more flexible and reliable. The collected 3D point cloud data, image information and geographic location information provide rich data support for the research of architectural environment, which helps to promote the development of related fields.

[0108] In a preferred embodiment of the present invention, the above step 14, processing the three-dimensional point cloud data, image information and geographic location information of the building environment, including point cloud stitching, denoising, registration and surface reconstruction to generate a three-dimensional model of the building, may include:

[0109] Step 141, stitching together a plurality of three-dimensional point cloud data collected by the laser radar at different positions and angles, and identifying and matching overlapping parts in the point cloud data;

[0110] Step 142, treating the points in the point cloud data as individuals, and initializing the group composed of each individual;

[0111] Step 143, defining a fitness function, and evaluating each individual to obtain an evaluation result of the fitness function;

[0112] Step 144, according to the evaluation result of the fitness function, the individual learns and updates its own state, and the learning and updating process is repeated until a preset number of iterations is reached to obtain a group learning result;

[0113] Step 145, according to the group learning result, the points marked as noise are removed to obtain denoised point cloud data;

[0114] Step 146, registering the denoised point cloud data with the image information captured by the camera to obtain registered point cloud data and image information;

[0115] Step 147, based on the registered point cloud data and image information, a surface fitting algorithm is used to construct a three-dimensional model including the building's geometric shape, structure, texture and color information.

[0116] In an embodiment of the present invention, multiple three-dimensional point cloud data files collected by a laser radar at different positions and angles are loaded from a storage medium (such as a hard disk). Each point cloud file contains a set of three-dimensional coordinates of points (as well as color, intensity, etc.). For each point in the source point cloud, find the nearest point in the target point cloud as the corresponding point. Using the coordinates of the corresponding points, calculate the transformation matrix (including rotation matrix and translation vector) required to align the source point cloud to the target point cloud. Use the calculated transformation matrix to transform the source point cloud to a new position. Repeat the above steps until the convergence condition is met (such as the error is less than a certain threshold or the number of iterations reaches an upper limit).

[0117] Specifically, let the source point cloud be , the target point cloud is .for Each point in ,exist Find the nearest point . Then, calculate the centroid (mean) of the two sets of corresponding points:

[0118] The centroid of the source point cloud ;in, Is the source point cloud The number of points in ; is the index of the point;

[0119] The centroid of the target point cloud ;

[0120] Next, calculate the covariance matrix ;in, is the transpose of the matrix;

[0121] Then, the covariance matrix is ​​used to perform singular value decomposition to solve the rotation matrix : ; (If det( ) < 0, then Need to be adjusted to ensure it is an orthogonal matrix); , is an orthogonal matrix; is a diagonal matrix.

[0122] Finally, calculate the translation vector .

[0123] Use the calculated rotation matrix and translation vectors , transform the source point cloud into the coordinate system of the target point cloud. Then, merge the transformed source point cloud with the target point cloud to form a spliced ​​point cloud. By comparing the coordinates of the points and removing duplicate points, noise and uneven surfaces can be reduced.

[0124] In step 142 , the point cloud data is segmented into smaller regions or clusters, each cluster containing a group of adjacent points.

[0125] Treat each point as an individual and initialize the group composed of each individual.

[0126] Step 143, extracting geometric features (such as distance, normal direction) or color features of the point. Define a fitness function that evaluates whether the point is noise or a valid data point based on the features of the point. For example, the fitness can be calculated based on factors such as the local density of the point, the similarity with neighboring points, etc.

[0127] Step 144, set the number of iterations or convergence conditions. Apply the fitness function to each individual to obtain an evaluation result. Based on the evaluation result, the individual learns and updates its own state. This involves moving the position of the point, adjusting the direction of its normal, etc. The evaluation and update process is repeated until the preset number of iterations is reached.

[0128] Step 145: Identify and mark noise points based on the group learning result, and remove the points marked as noise from the point cloud data to obtain denoised point cloud data.

[0129] Step 146, use PCL (Point Cloud Library) to load the point cloud data. Estimate the normal of each point in the point cloud. Apply an edge detection algorithm, such as EdgeAwareFeatureEstimation in PCL, to identify edge points in the point cloud. Calculate local curvature on the point cloud by analyzing the geometry of each point and its neighborhood points, and identify points with significant changes in curvature as corners. Use the 3D SIFT (Scale-Invariant Feature Transform) algorithm to extract key points in the point cloud. Calculate feature descriptors for each key point, which are invariant to rotation, scale changes, etc. Compare the feature descriptors in the point cloud and the image, use Euclidean distance to find the most similar feature pairs, and establish a preliminary list of feature correspondences. Use the RANSAC (Random Sample Consensus) algorithm to filter out false matches. RANSAC estimates the transformation by randomly selecting feature pairs and verifies whether other feature pairs are consistent with the transformation. Feature pairs verified by RANSAC are retained as reliable matches.

[0130] Based on reliable feature correspondences, the least squares method is used to calculate the preliminary transformation matrix from the point cloud to the image, including rotation and translation parameters. The ICP (Iterative Closest Point) algorithm is used to fine-tune the preliminary transformation. ICP optimizes the transformation by iteratively minimizing the closest point distance between the point cloud and the image (or its transformed position). The ICP process is repeated until convergence conditions are reached. A unified coordinate system is determined as a reference, and the coordinate system of the point cloud is selected here. The error after registration, such as the mean square error, is calculated to evaluate the accuracy of the registration. Use 3D visualization tools (such as PCL Viewer, MeshLab, CloudCompare, etc.) to visually view the registered point cloud and image (the image is associated with the point cloud through texture mapping). Check in the visualization tool whether the point cloud is aligned with the image and whether there are obvious errors or inconsistencies.

[0131] Step 147, select a suitable surface fitting algorithm based on the characteristics of the registered point cloud data and the target application. For the building model, triangulation meshing (such as the Poisson reconstruction algorithm) is used to process the registered point cloud data to generate a preliminary three-dimensional model. This step involves sub-steps such as point cloud segmentation, normal estimation, and mesh generation. For the Poisson reconstruction algorithm, appropriate parameters such as depth and resolution need to be set to control the accuracy and detail of the reconstruction. For noise, missing parts or irregular shapes in the point cloud data, additional processing such as filtering, interpolation or manual repair is required. Ensure that the registered image information (including texture and color) matches the point cloud data in space and resolution.

[0132] Texture mapping is used to map the texture and color from the image information onto the surface of the 3D model. This usually involves associating the pixels of the image with the vertices or faces of the 3D model. During the texture mapping process, problems such as texture seams or overlaps may occur. Texture stitching is needed to reduce these visual artifacts.

[0133] Apply smoothing algorithms (such as Laplace smoothing) to reduce noise and unevenness on the surface of the 3D model. This helps improve the visualization quality and rendering speed of the model. Use visualization tools or software to carefully check the constructed 3D model to ensure that the model's geometry, texture mapping, and color are as expected. Save the constructed and optimized 3D model in an appropriate file format (such as OBJ, STL, PLY, etc.) for subsequent application and rendering.

[0134] Assume that there is a scene containing multiple buildings, and the LiDAR collects multiple three-dimensional point cloud data from different positions and angles. Through the stitching process of step 141, these point cloud data are stitched into a complete scene point cloud. In step 142, the point cloud is divided into multiple clusters, and each point is initialized as an individual. Next, in step 143, a fitness function is defined, which evaluates whether it is noise or a valid data point based on the local density of the point. Through the group learning process of step 144, noise points (such as leaves, pedestrians, etc.) are identified and removed. In step 146, the denoised point cloud data is aligned with the image information captured by the camera to ensure that the position of the point cloud and the image in space is consistent. Finally, in step 147, a three-dimensional model containing the building geometry, structure, texture and color information is constructed using surface fitting algorithms and texture mapping techniques.

[0135] Through point cloud stitching and denoising, a more accurate and complete three-dimensional model of the building environment can be obtained. The texture and color in the image information are mapped to the three-dimensional model to make the model more realistic and vivid. The constructed three-dimensional model can be used in a variety of application scenarios such as building information modeling (BIM), urban planning, virtual reality (VR) and augmented reality (AR). Through group learning and optimization algorithms, large-scale point cloud data and image information can be processed more efficiently and the processing time can be shortened.

[0136] In a preferred embodiment of the present invention, the calculation formula of the fitness function is: ;

[0137] in, Indicate point The fitness value of Represents a point in a 3D point cloud; represents the consistency weight coefficient of the normal vector; Indicates the number of neighborhood points; Indicate point The normal vector of Indicate point Neighborhood points The normal vector of represents the tangent vector consistency weight coefficient; Indicates the number of neighborhood points used to calculate the average of tangent vector differences; Indicate point The tangent vector of Indicate point Neighborhood points The tangent vector of represents the Gaussian curvature weight coefficient; Indicate point Gaussian curvature of represents the mean curvature weight coefficient; Indicate point The number of points in the surrounding neighborhood; Represents an index variable; Indicate point The surrounding neighborhood The curvature of a point; represents the curvature variance weight coefficient; Represents the point density weight coefficient; Indicate point The volume of the surrounding neighborhood.

[0138] In the embodiment of the present invention, the weight coefficient is determined (Normal consistency weight), (tangent vector consistency weight), (Gaussian curvature weight), (mean curvature weight), (curvature variance weight), (Point density weight). Set the number of neighborhood points, (used to calculate normal vector differences), (used to calculate tangent vector differences).

[0139] For point , calculate its normal vector .Traversal point of Neighborhood points, calculate each neighborhood point The normal vector . Calculate the average of the normal vector differences . Calculate the normal vector consistency term .

[0140] For point , calculate its tangent vector .Traversal point of Neighborhood points for calculating tangent vector differences. Calculate each neighborhood point The tangent vector . Calculate the average of the tangent vector differences . Calculate the tangent vector consistency term Calculation point Gaussian curvature . Calculate the Gaussian curvature term .

[0141] Traversal Points The points in the surrounding neighborhood, calculating the curvature of each point .

[0142] Calculate mean curvature .

[0143] Calculate the mean curvature term .

[0144] Use the calculated curvature value , calculate the variance of the curvature and calculate the curvature variance term Calculation point The volume of the surrounding neighborhood . Calculate point density (in, is the number of points in the neighborhood). Calculate the point density term Adding all the above terms together, we get The fitness value of .

[0145] Through the normal vector consistency term (given by Control), which can accurately capture the difference between the normal vector of a point and its neighboring points, thereby strengthening the geometric structure information of the point cloud data. This helps to more accurately understand and utilize the geometric features of the point cloud in subsequent processing, such as point cloud segmentation and feature extraction. Control) focuses on the consistency of the tangent vector between a point and its neighboring points, providing an effective means of quantifying the tangent features of point cloud data. ) and the mean curvature term (controlled by Control) jointly considers the curvature characteristics of the points, providing a comprehensive perspective for curvature analysis of point cloud data. This helps to identify key features such as convexities, concavities, and edges in the point cloud, laying the foundation for further point cloud processing (such as feature point extraction and shape analysis). The curvature variance term (given by Control) can accurately describe the local curvature changes of point cloud data by calculating the variance of curvature in the neighborhood around the point. This helps to capture more subtle geometric changes in the point cloud and improve the analysis accuracy of point cloud data. Control) takes into account the spatial distribution density of points, providing strong support for the density analysis of point cloud data.

[0146] In a preferred embodiment of the present invention, the above step 15 automatically calculates the basic dimensions, area, and volume measurement parameters of the building based on the three-dimensional model of the building, extracts the key structural features of the building, performs spatial layout analysis, and structural stability assessment measurement tasks, and obtains comprehensive measurement and analysis results, which may include:

[0147] Step 151, using the three-dimensional model data, calculating the basic size of the building, the area of ​​each surface and the total volume of the building, and obtaining the basic size of the building, the area of ​​each surface and the total volume data;

[0148] Step 152, analyzing the basic dimensions, surface areas and total volume data of the building, identifying key structural features of the building, including beam and column locations, wall types, and door and window locations, and extracting geometric information and attribute information of the key structural features from the three-dimensional model of the building;

[0149] Step 153, sorting out the geometric information and attribute information of the extracted key structural features to form a structural feature data set, and numbering and classifying each feature in the structural feature data set to obtain a numbered and classified key structural feature data set;

[0150] Step 154, dividing the building space into different areas according to the three-dimensional model of the building, the numbered and classified key structural feature data set, and determining the purpose and function of each area;

[0151] Step 155, analyzing the layout of each area, including space size, shape, ventilation, and lighting surface, to evaluate the rationality of the layout and obtain a spatial layout analysis result;

[0152] Step 156, establishing a structural analysis model of the building according to the spatial layout analysis results, the three-dimensional model of the building, and the numbered and classified key structural feature data set;

[0153] Step 157, performing stress analysis on the structural analysis model of the building, simulating the stress of the building under specific loads, including wind load, earthquake, and deadweight, and evaluating the stability of the building under the specific loads to obtain a structural stability evaluation report;

[0154] Step 158, the basic dimensions of the building, the surface area and total volume data, the spatial layout analysis results and the structural stability assessment report are summarized to obtain a comprehensive measurement and analysis result.

[0155] In an embodiment of the present invention, a 3D model file (such as .rvt, .skp, .obj, etc.) of a building is loaded using appropriate software or libraries (such as Autodesk Revit, SketchUp, Blender, etc.). Ensure that the model file contains complete geometric shapes, vertex coordinates, patch information, etc. Traverse the vertex coordinates of the 3D model to find the farthest point and the closest point of the model in the X, Y, and Z axis directions. Calculate the distance between the farthest point and the closest point to obtain the length, width, and height of the building. For each surface (patch), obtain its vertex coordinates. Calculate the area of ​​the triangle using the vector cross multiplication and modulus method. Add up the areas of all surfaces to obtain the total surface area of ​​the building. Perform volume segmentation on the 3D model and divide it into multiple simple geometric bodies (such as cuboids, tetrahedrons, etc.). Use the volume formula of the geometric body to calculate the volume of each simple geometric body.

[0156] Add up the volumes of all geometric bodies to get the total volume of the building.

[0157] Step 152, using a shape analysis algorithm to identify key structural features such as beams, columns, walls, doors and windows in the three-dimensional model. Using an edge detection algorithm to extract the edge contours of the features. Applying a topological analysis algorithm to analyze the connection relationship between the features. For each identified feature, obtain its geometric information such as position (coordinates), size (length, width, height), direction (normal vector), etc. Extract attribute information of the feature from the metadata of the three-dimensional model, such as material type, structure type (such as load-bearing wall, non-load-bearing wall), door and window opening method, etc. If the metadata does not contain the required information, supplement it according to the design documents or specifications.

[0158] Step 153, define a structured data set, including fields such as feature number, type, geometric information, attribute information, etc. Assign a unique number to each feature in the data set. Classify and store the data according to feature type (such as beams, columns, walls, doors and windows).

[0159] Step 154, based on the 3D model and the key structural feature data set, the building space is divided into different areas using a space partitioning algorithm. The division is performed taking into account factors such as the room boundaries, the location of the corridor, and the layout of the stairs. The purpose and function of each area are determined based on the geometric features, positional relationships, and attribute information (such as room names and function labels) of the area. The purpose and function are compared with the building design documents or specifications to ensure the accuracy of the purpose and function.

[0160] Step 155, . Set reasonable space utilization thresholds according to building design specifications and industry standards. For example, for office space, the space utilization rate should be no less than 70%. Use fluid dynamics simulation software (such as CFD software) to simulate the air flow inside the building and calculate the air exchange rate and ventilation efficiency. Set the minimum value of ventilation efficiency according to building ventilation design standards and industry specifications. For example, for residential buildings, at least 0.5 air changes should be made per hour. Use lighting simulation software (such as Radiance, DIVA, etc.) to simulate the natural light distribution inside the building and calculate the daylight factor and illuminance distribution. Set the minimum value of the daylight factor and the uniformity requirements of illuminance according to building lighting design standards and industry specifications. For example, for office space, the daylight factor should be no less than 1%, and the illuminance distribution should be uniform. For each area, measure its actual use area and total area. Use the above formula to calculate the space utilization rate. Build a 3D model of the building and import it into the fluid dynamics simulation software. Set boundary conditions and initial conditions, such as external wind speed, temperature, openings inside the building, etc. Run the simulation to obtain air flow and ventilation efficiency data. Build a 3D model of the building and import it into the lighting simulation software. Set parameters such as the sun position, sky model, and reflectivity of building materials. Run the simulation to obtain daylighting factor and illuminance distribution data. Compare the space utilization of each area with the set standard value and identify areas with low utilization.

[0161] Analyze the ventilation efficiency simulation results to identify areas with poor ventilation or vortexes. Evaluate the lighting quality of each area based on the daylight factor and illumination distribution data to identify areas with insufficient lighting or uneven illumination.

[0162] For areas with low utilization, consider replanning the space layout, adding functional areas or optimizing furniture layout. For areas with poor ventilation, consider adding openings, adjusting the location of doors and windows, or installing ventilation equipment. For areas with vortices, consider optimizing the building shape or internal partitions. For areas with insufficient lighting, consider increasing the number or area of ​​windows, optimizing the location of windows, or installing reflective materials. For areas with uneven illumination, consider adjusting the layout of lamps or using lamps with adjustable light.

[0163] Step 156, simplify the three-dimensional model according to the spatial layout analysis results and the key structural feature data set. Keep the elements that are important for structural analysis, such as beams, columns, load-bearing walls, etc. Use the simplified three-dimensional model and the extracted key structural feature information to establish a structural analysis model of the building. Ensure that the model can reflect the mechanical properties and structural characteristics of the building.

[0164] Step 157, depending on the complexity and specific needs of the building project, select a suitable tool from professional load analysis software such as ANSYS, ABAQUS, SAP2000, etc. These software have powerful structural analysis capabilities and can handle complex loading conditions and material properties. Make sure the selected software has been correctly installed and configured with corresponding modules and plug-ins according to project requirements. Before importing, make sure that the structural analysis model has been simplified and saved in a format that the software can recognize (such as .step, .iges, .dxf, etc.) according to the previous steps.

[0165] Open the stress analysis software and use the import function to import the structural analysis model into the software. Check whether the model is complete and accurate, and make necessary corrections. In the material library of the software, define the physical and mechanical properties of each material in the model, such as elastic modulus, Poisson's ratio, density, yield strength, etc., according to the building design documents or specifications.

[0166] Assign defined materials to the corresponding parts in the model, ensuring that each component has the correct material properties. Define various load conditions such as wind load, earthquake, self-weight, etc. according to the building design specifications and project requirements. For each load, you need to set its size, direction, and location. Apply the defined loads to the model, ensuring that the location and method of application of the loads are consistent with the actual situation. Set the analysis parameters such as solver type, number of iterations, convergence criteria, etc. according to the requirements of the software and the needs of the project. Start the force analysis software and solve the model. The software will calculate the stress, strain, and displacement of the model under a specific load based on the set material properties, load conditions, and analysis parameters. View the stress distribution diagram generated by the software to understand the stress distribution of the model under the action of load. Identify stress concentration areas and possible structural weaknesses. View the deformation diagram to understand the deformation of the model under the action of load. Evaluate whether the deformation is within the design allowable range and identify possible areas of excessive deformation.

[0167] Based on the stress distribution diagram and deformation diagram, evaluate the stability of the building under specific loads. Check whether there are unstable phenomena such as yielding, breaking or excessive deformation of components. Compare the analysis results with the requirements of the building design specifications or standards to ensure that the structural performance of the building meets the regulations. Based on the analysis results, identify potential structural weaknesses or risk points in the model, such as stress concentration areas, areas of excessive deformation, insufficient material performance, etc. Conduct risk assessment on the identified structural weaknesses or risk points to determine the extent of their impact on the overall stability of the building.

[0168] Step 158, summarize the basic dimensions of the building, surface areas, total volume data, spatial layout analysis results and structural stability assessment report. Use appropriate report generation tools (such as Word, Excel, PowerPoint, etc.) to generate a comprehensive measurement and analysis report. The report should contain all important data, analysis results and conclusions, presented in the form of charts, text, etc. Ensure that the report is easy to understand and can provide valuable reference for building design, construction and maintenance.

[0169] Suppose there is a new office building project that requires comprehensive 3D model analysis, spatial layout assessment, and structural stability assessment. Use Autodesk Revit for 3D modeling, SketchUp for model simplification and visualization, ANSYS for structural analysis, and generate a comprehensive measurement and analysis report.

[0170] Specifically, use Autodesk Revit to load the .rvt format 3D model file of the office building. Make sure the model contains complete geometric shapes, vertex coordinates, patch information, etc. Traverse the vertex coordinates of the 3D model, find the farthest and closest points of the model in the X, Y, and Z axis directions, and calculate that the length of the office building is 50 meters, the width is 30 meters, and the height is 20 meters. For each surface (patch), obtain its vertex coordinates.

[0171] Use the vector cross product and modulus method to calculate the area of ​​the triangle. Add up the areas of all the surfaces and you get the total surface area of ​​the office building to be 3,000 square meters.

[0172] The 3D model is segmented into multiple simple geometric bodies such as cuboids and tetrahedrons. The volume formula of each simple geometric body is used to calculate the volume of each simple geometric body. The volumes of all geometric bodies are accumulated to obtain the total volume of the office building, which is 30,000 cubic meters. The shape analysis algorithm is used to identify key structural features such as beams, columns, walls, doors and windows in the 3D model. The edge detection algorithm is used to extract the edge contours of the features, such as the outlines of beams and columns, the frames of doors and windows, etc. The topological analysis algorithm is used to analyze the connection relationship between features, such as the connection between walls and beams and columns, and the relationship between doors and windows and walls. The geometric information of each feature, such as position (coordinates), size (length, width, height), direction (normal vector), etc., is obtained, and its attribute information, such as material type (concrete, steel, etc.), structure type (load-bearing wall, non-load-bearing wall, etc.), and the opening method of doors and windows, etc., is extracted.

[0173] Use the space partitioning algorithm to divide the office building space into different areas, such as offices, meeting rooms, corridors, stairwells, etc. Determine the purpose and function of each area based on the geometric characteristics, positional relationships, and attribute information of the area. Measure the actual use area and total area of ​​each area and calculate the space utilization rate. It is found that the space utilization rate of a certain office is only 60%, which is lower than the set threshold of 70%. Consider replanning the layout of this area. Use CFD software to perform fluid dynamics simulation, set boundary conditions and initial conditions such as external wind speed, temperature, and openings inside the building. Run the simulation to obtain air flow and ventilation efficiency data. It is found that a corridor area is not well ventilated. Consider adding openings or installing ventilation equipment. Use Radiance software to perform lighting simulation, set parameters such as sun position, sky model, and reflectivity of building materials. Run the simulation to obtain daylighting factor and illuminance distribution data. It is found that a certain office is not well lit. Consider increasing the number of windows or optimizing the window position. Simplify the three-dimensional model based on the spatial layout analysis results and key structural feature data sets. Retain elements that are important for structural analysis, such as beams, columns, and load-bearing walls. Using the simplified 3D model and the extracted key structural feature information, a structural analysis model of the office building was established in ANSYS.

[0174] Define the physical and mechanical properties for each material in the model in ANSYS. Apply load conditions such as wind load, earthquake, and self-weight. Set analysis parameters such as solver type, number of iterations, and convergence criteria. Solve the model and obtain analysis results such as stress distribution diagrams and deformation diagrams. Evaluate the stability of the office building under specific loads and find that stress concentration is found at a certain beam-column connection. Consider strengthening the structural design of this area. Summarize the basic dimensions of the office building (50 meters long, 30 meters wide, and 20 meters high), total surface area (3,000 square meters), total volume (30,000 cubic meters), spatial layout analysis results (such as low space utilization in a certain office), ventilation and lighting analysis results (such as poor ventilation in a corridor and insufficient lighting in a certain office), and structural stability assessment reports (such as stress concentration at a certain beam-column connection). Generate a comprehensive measurement and analysis report using tools such as Word and Excel. The report contains all important data, analysis results, and conclusions, presented in the form of charts, text, etc. Ensure that the report is easy to understand and can provide valuable reference for the architectural design, construction, and maintenance of the office building.

[0175] Through automated calculation and analysis, the accuracy and efficiency of building measurement can be improved, and the errors and time costs of manual measurement and calculation can be reduced. The comprehensive measurement and analysis results can fully reflect the basic physical parameters of the building, such as size, area, volume, and performance indicators such as spatial layout and structural stability, and provide a comprehensive reference for building design, construction and maintenance. Through spatial layout analysis and structural stability assessment, problems or deficiencies in building design can be found, providing a scientific basis for optimizing building design. Through force analysis and stability assessment, potential risk points or structural weaknesses in the building can be identified, and timely measures can be taken to improve them and improve the safety of the building. The comprehensive measurement and analysis results can be stored and shared in a digital form, promoting the integration and sharing of building information, and providing support for the informatization and intelligent development of the construction industry.

[0176] In a preferred embodiment of the present invention, the above step 16 automatically generates a measurement report based on the comprehensive measurement and analysis results, including a three-dimensional model of the building, various measurement parameter tables and a spatial analysis report, which may include:

[0177] In the embodiment of the present invention, a spatial analysis report is written based on the analysis results, including the analysis purpose, method, results and conclusions. The report should include auxiliary explanatory materials such as charts, photos and 3D model screenshots.

[0178] The report structure is:

[0179] Cover: includes report title, compiling unit, compiling date, etc.

[0180] Table of Contents: Lists the main contents and page numbers of the report.

[0181] Introduction: This describes the background, purpose and scope of the report.

[0182] 3D model display: Display the 3D model of the building in the form of pictures or PDF.

[0183] Measurement parameter table: lists the detailed data of all measurement parameters.

[0184] Spatial Analysis Report: Detailed description of the results and conclusions of the spatial analysis.

[0185] Appendix: includes additional information such as measurement data, analysis scripts, references, etc.

[0186] As the building is used and modified, take measurements and update reports regularly. Track changes to the building to ensure reports are up-to-date and accurate.

[0187] like Figure 2 As shown, an embodiment of the present invention further provides a building measurement system based on an intelligent robot, comprising:

[0188] Intelligent robotic modules for autonomous navigation, positioning and traversal of the interior and exterior spaces of buildings, and for 3D scanning and data collection;

[0189] The data processing module is used to receive the three-dimensional scanning data collected by the intelligent robot, and perform pre-processing, denoising, and splicing to generate a three-dimensional model of the building;

[0190] The feature extraction module is used to analyze the three-dimensional model of the building, automatically identify and extract key structural features, including the location of beams and columns, wall types, and the location of doors and windows, and extract the geometric information and attribute information of key structural features;

[0191] The spatial layout analysis module is used to divide the building space into different areas according to the 3D model of the building and the extracted key structural features, and determine the purpose and function of each area; analyze the layout of each area, including space size, shape, ventilation, and lighting surface;

[0192] The structural stability assessment module is used to establish a structural analysis model using the building 3D model and key structural feature data, and to perform stress analysis on the structural analysis model, assess the stability of the building under specific loads, and generate a comprehensive measurement and analysis report;

[0193] The user interaction interface module is used to provide a user interaction interface, allowing users to set measurement parameters, view measurement progress, obtain measurement results and analysis reports.

[0194] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0195] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0196] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0197] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A building measurement method based on an intelligent robot, characterized in that: The method comprises: Deploy the intelligent robot to the building area to be tested; Determine the final driving path of the intelligent robot based on the layout of the building area to be tested; The intelligent robot automatically drives along the final driving path and uses the internal sensor components to collect 3D point cloud data, image information and geographic location information of the building environment; Processing of 3D point cloud data, image information and geographic location information of the building environment, including point cloud stitching, denoising, registration and surface reconstruction, to generate a 3D model of the building; According to the 3D model of the building, the basic dimensions, area, and volume measurement parameters of the building are automatically calculated, and the key structural features of the building are extracted to perform spatial layout analysis and structural stability assessment measurement tasks to obtain comprehensive measurement and analysis results; Automatically generate measurement reports based on comprehensive measurement and analysis results, including the 3D model of the building, various measurement parameter tables and space analysis reports; According to the layout of the building area to be tested, the final driving path of the intelligent robot is determined, including: The building area to be tested is taken as a search space to be solved, and the building area to be tested is divided into multiple sub-areas, wherein the multiple sub-areas are used as initial branch nodes; Define evaluation criteria for each branch node, and start from the current node and continuously branch out new child nodes; According to the evaluation criteria, the evaluation value of each new child node is calculated, and the candidate nodes for constructing the final path are identified by comparing the evaluation values ​​of different child nodes; Continue to explore and delimit the search space. When the search space reaches the preset exploration and delimitation conditions, the intelligent robot determines the final driving path from the starting point to the end point based on the identified candidate nodes; The calculation formula for the evaluation value of each new child node is: ; in, Indicates the evaluation value of the new child node; , , , , , , They represent the weight coefficients of path length, obstacles, feature points, energy consumption, wind direction, temperature, and humidity respectively; represents the path length; Indicates the area of ​​the obstacle; Indicates the total area; Indicates the obstacle type coefficient; Indicates the obstacle type; Indicates the number of feature points; Indicates the priority of feature points; Indicates the feature point type coefficient; Indicates the number of feature point types; An index representing the type of feature point; Indicates The type of feature points; Indicates The number of feature points; represents the terrain coefficient; Indicates load influence coefficient; represents the robot load; Energy consumption coefficient indicating speed; Indicates the current speed of the robot; Indicates the maximum speed of the robot; Indicates the energy consumption coefficient of the load; Energy consumption coefficient indicating external conditions; Indicates wind direction; Indicates temperature; indicates humidity.

2. The building measurement method based on intelligent robots according to claim 1 is characterized in that: The intelligent robot automatically drives along the final driving path and uses internal sensor components to collect 3D point cloud data, image information and geographic location information of the building environment, including: The intelligent robot automatically drives according to the final driving path; During driving, the laser radar continuously emits laser beams and receives reflected signals. It calculates the three-dimensional coordinate information of the building environment through the time difference and angle information of the reflected signals to form three-dimensional point cloud data. The camera captures image information of the building environment at the same frame rate as the lidar, including the appearance, texture, and color of the building; the positioning system receives satellite signals in real time, calculates and records the geographic location information of the intelligent robot, including longitude, latitude, and altitude.

3. The building measurement method based on intelligent robots according to claim 2 is characterized in that: Process the 3D point cloud data, image information and geographic location information of the building environment, including point cloud stitching, denoising, registration and surface reconstruction, to generate a 3D model of the building, including: Splice multiple 3D point cloud data collected by LiDAR at different positions and angles, and identify and match the overlapping parts in the point cloud data; Treat the points in the point cloud data as individuals and initialize the group composed of each individual; Define a fitness function and evaluate each individual to obtain the evaluation result of the fitness function; According to the evaluation results of the fitness function, individuals learn and update their own states, and the learning and updating process is repeated until the preset number of iterations is reached to obtain the group learning results; According to the group learning results, the points marked as noise are removed to obtain denoised point cloud data; The denoised point cloud data is registered with the image information captured by the camera to obtain the registered point cloud data and image information; Based on the registered point cloud data and image information, a surface fitting algorithm is used to construct a three-dimensional model containing the building's geometric shape, structure, texture and color information.

4. The building measurement method based on intelligent robots according to claim 3 is characterized in that: The calculation formula of the fitness function is: ; in, Indicate point The fitness value of Represents a point in a 3D point cloud; represents the consistency weight coefficient of the normal vector; Indicates the number of neighborhood points; Indicate point The normal vector of Indicate point Neighborhood points The normal vector of represents the tangent vector consistency weight coefficient; Indicates the number of neighborhood points used to calculate the average of tangent vector differences; Indicate point The tangent vector of Indicate point Neighborhood points The tangent vector of represents the Gaussian curvature weight coefficient; Indicate point Gaussian curvature of represents the mean curvature weight coefficient; Indicate point The number of points in the surrounding neighborhood; Represents an index variable; Indicate point The surrounding neighborhood The curvature of a point; represents the curvature variance weight coefficient; Represents the point density weight coefficient; Indicate point The volume of the surrounding neighborhood.

5. The intelligent robot-based building measurement method according to claim 4, characterized in that: According to the 3D model of the building, the basic dimensions, area, and volume measurement parameters of the building are automatically calculated, and the key structural features of the building are extracted to perform spatial layout analysis and structural stability assessment measurement tasks, and obtain comprehensive measurement and analysis results, including: Using the three-dimensional model data, the basic dimensions of the building, the area of ​​each surface and the total volume of the building are calculated to obtain the basic dimensions of the building, the area of ​​each surface and the total volume data; Analyze the basic dimensions, surface areas and total volume data of the building, identify the key structural features of the building, including the location of beams and columns, wall types, and the location of doors and windows, and extract the geometric information and attribute information of the key structural features from the 3D model of the building; The geometric information and attribute information of the extracted key structural features are sorted to form a structural feature data set, and each feature in the structural feature data set is numbered and classified to obtain a numbered and classified key structural feature data set; Based on the 3D model of the building, the numbered and classified key structural feature datasets, the building space is divided into different areas, and the purpose and function of each area are determined; Analyze the layout of each area, including space size, shape, ventilation, and lighting, to evaluate the rationality of the layout and obtain the spatial layout analysis results; Establish a structural analysis model of the building based on the spatial layout analysis results, the three-dimensional model of the building, and the numbered and classified key structural feature data sets; Conduct stress analysis on the structural analysis model of the building, simulate the stress of the building under specific loads, including wind load, earthquake, and deadweight, and evaluate the stability of the building under specific loads to obtain a structural stability assessment report; The basic dimensions of the building, surface area and total volume data, spatial layout analysis results and structural stability assessment report are summarized to obtain comprehensive measurement and analysis results.

6. A building measurement system based on an intelligent robot, the system implementing the method as claimed in any one of claims 1 to 5, characterized in that: include: Intelligent robotic modules for autonomous navigation, positioning and traversal of the interior and exterior spaces of buildings, and for 3D scanning and data collection; The data processing module is used to receive the three-dimensional scanning data collected by the intelligent robot, and perform pre-processing, denoising, and splicing to generate a three-dimensional model of the building; The feature extraction module is used to analyze the three-dimensional model of the building, automatically identify and extract key structural features, including the location of beams and columns, wall types, and the location of doors and windows, and extract the geometric information and attribute information of key structural features; The spatial layout analysis module is used to divide the building space into different areas according to the 3D model of the building and the extracted key structural features, and determine the purpose and function of each area; analyze the layout of each area, including space size, shape, ventilation, and lighting surface; The structural stability assessment module is used to establish a structural analysis model using the building 3D model and key structural feature data, and to perform stress analysis on the structural analysis model, assess the stability of the building under specific loads, and generate a comprehensive measurement and analysis report; The user interaction interface module is used to provide a user interaction interface, allowing users to set measurement parameters, view measurement progress, obtain measurement results and analysis reports.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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