Decoration project inspection system based on bionic robot dog

Abstract

The invention discloses a decoration project inspection system based on a bionic robot dog, relates to the technical field of decoration project inspection, and aims to significantly reduce manual inspection time and labor cost through autonomous inspection of the robot dog, improve the overall acceptance efficiency, collect and analyze data in real time, quickly generate a detection result and improve the inspection efficiency. Delay in a traditional method is avoided, the AI technology and image recognition are utilized, interference of human factors is systematically eliminated, objectivity and consistency of acceptance results are ensured, decoration quality is accurately recognized and evaluated through a high-precision sensor and an algorithm, the risks of missing detection and misjudgment are reduced, and the efficiency is improved through an automatic process. The acceptance personnel can concentrate more energy on important decision and management, the working efficiency is improved, the application of the system conforms to the development direction of buildings and smart cities, and the building industry is promoted to develop in the more efficient and more intelligent direction.

Description

Technical Field

[0001] The present invention relates to the technical field of decoration engineering inspection, and in particular to a decoration engineering inspection system based on a bionic robot dog. Background Art

[0002] With the rapid development of the construction industry and the continuous advancement of technology, the quality control and safety management of decoration projects have become increasingly important. Traditional inspection methods for decoration projects usually rely on manual inspections, which is not only inefficient but also easily affected by human factors, leading to omissions or misjudgments. In order to improve the quality management level of decoration projects and ensure construction safety, the market urgently needs a more efficient and intelligent inspection solution. Therefore, a decoration project inspection system based on a bionic robot dog is needed.

[0003] Prior art, such as the invention patent application with announcement number CN114745399A, discloses an intelligent construction project quality acceptance management system, which uses an image recognition detection module to accurately identify the area to be accepted from the acceptance image, and detects the area to be accepted based on the recognition result to generate detection data. The data comparison module obtains the corresponding relevant acceptance rules from the acceptance rule generation module based on the recognition result, and compares and analyzes the detection data with the relevant acceptance rules. The acceptance information generation module generates relevant acceptance information, and the user network node authenticates the acceptance information, thereby achieving the goal of comprehensively evaluating the quality of the construction project while ensuring the accuracy of the final acceptance result of the acceptance project. The technical solution provided by the present invention can effectively overcome the defects of the prior art that the acceptance result is poor in accuracy and cannot comprehensively evaluate the quality of the construction project.

[0004] In view of the above scheme, the applicant of the present invention has found that the above technology has at least the following technical problems: 1. The traditional acceptance method relies on manual inspection, which is easily affected by personal experience and subjective judgment and lacks objectivity; in the actual acceptance of building decoration and renovation, the number of inspection batches is large, the material technology is updated quickly, the acceptance of personnel is difficult and the efficiency is low, and manual acceptance usually takes a long time to complete, especially in large-scale construction projects, the efficiency is low, and it cannot meet the needs of rapid acceptance. Long-term manual inspections can easily lead to personnel fatigue, increasing the risk of errors in inspections.

[0005] 2. Traditional methods make it difficult to conduct a comprehensive and integrated assessment of all aspects of a construction project. They often only focus on surface issues and ignore potential deep-seated quality risks. They lack systematic assessment processes and standards, which makes it difficult to effectively compare and analyze the acceptance results of different projects. At the same time, it is often difficult to quickly adapt to new materials and construction process changes, which affects acceptance standards and results. The experience and judgment standards of various acceptance personnel are different, resulting in a lack of standardization and consistency in acceptance results. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a decoration project inspection system based on a bionic robot dog.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a decoration project inspection system based on a bionic robot dog, including: an acceptance preparation module: used to place a robot dog in the target acceptance building, prepare the robot dog, and plan the route of the robot dog that has completed the preparation work.

[0008] Decoration quality acceptance module: When the robot dog is conducting acceptance processing on each floor of the target acceptance building, it will determine whether each scanned object on each floor needs early warning feedback. If a scanned object on a certain floor needs early warning feedback, the scanned object on that floor will be subjected to quality early warning feedback.

[0009] Overall acceptance evaluation module: used to evaluate whether the overall construction quality of each floor in the target acceptance building meets the standards, and to provide evaluation feedback on the acceptance results of each floor in the target acceptance building.

[0010] Preferably, the robot dog is prepared, and the specific preparation process is as follows: A1. Inspection preparation: adjust the robot dog to a squatting position, place it in the plane area of ​​the target acceptance building, and the ground in the plane area is flat. At the same time, there are no obstacles within a range of 1 meter around the robot dog. Check whether the battery indicator light of the robot dog is on, and confirm that it has sufficient power. Make sure that the battery is correctly installed in the battery slot without looseness or poor contact. If the battery is insufficient, charge or replace the battery in time. After confirming that all the above checks are correct, lightly press the power switch of the robot dog to turn on the power system of the robot dog.

[0011] A2. Exercise preparation: Use a mobile device to connect to the robot dog's WiFi network, ensure a stable connection, open the software interface, access the robot dog's control system, remotely access the robot dog's perception host through the connected device, select the exercise mode and command in the user interface, set the robot dog's action parameters, and after confirming that the set exercise command is correct, click the send button, the command will be transmitted to the robot dog in real time, and then the robot dog will start to move according to the received command, and adjust the direction and speed as needed.

[0012] A3. Obstacle avoidance preparation: The built-in ultrasonic radar of the robot dog starts working, scanning the environment ahead in real time to determine whether there are any obstacles. The robot dog will periodically send and receive ultrasonic signals to measure the distance to the obstacle ahead. Combined with the robot dog's built-in algorithm, the detected data is analyzed. If an obstacle appears ahead, the robot dog will quickly calculate the best obstacle avoidance path to ensure its safety. After confirming that there is an obstacle ahead, the robot dog will actively adjust its direction of travel and accelerate or decelerate.

[0013] Preferably, the robot dog that has completed the preparation work will plan its route, and the specific planning process is as follows: B1. When the robot dog is powered on and in standby mode, an initialization instruction is sent through the user interface to start the robot dog's system. During this process, the robot dog will perform a self-check to ensure that each sensor and system module is operating normally.

[0014] B2. The robot dog will start the lidar system to conduct a comprehensive environmental scan. The lidar emits laser beams through high-speed rotation to collect three-dimensional spatial data of the surrounding environment. This data will be transmitted back to the internal processing system in real time. The robot dog will automatically start the point cloud mapping program to convert the scanned laser data into point cloud data.

[0015] B3. After the point cloud data is processed, the robot dog will generate a detailed point cloud map, which accurately reflects the geometric shapes and spatial relationships of the surrounding buildings. The robot dog converts the point cloud map into a raster map, where the raster map divides the environment into regular grids to facilitate subsequent algorithms for path planning and positioning. Each grid represents whether there are passable or impassable obstacles in the unit space.

[0016] B4. Using the HDL-localization positioning algorithm, the robot dog will accurately locate itself based on the grid map and sensor data. The robot dog can update its position and posture in the environment in real time to ensure highly accurate feedback on its own dynamic state. Based on the determined starting point, end point and environmental information, the robot dog will automatically plan the inspection path and generate an optimized inspection route.

[0017] Preferably, the decoration quality acceptance module also includes a data acquisition unit and a data analysis unit, wherein the data acquisition unit is used to collect basic information corresponding to each scanned object on each floor, and the basic information includes size, flatness, proportion of each material and distance corresponding to each other decoration material.

[0018] The data analysis unit is used to analyze and obtain the decoration quality evaluation coefficient corresponding to each scanned object on each floor according to the basic information corresponding to each scanned object on each floor.

[0019] Preferably, the judgment of whether each scanned object in each floor needs early warning feedback is performed as follows: C1. The robot dog uses YOLOv8 cutting vision to identify the type of each scanned object in each floor, and compares the type of each scanned object in each floor with the scanned object type corresponding to each standard decoration quality assessment coefficient in the database, and then obtains the standard decoration quality assessment coefficient corresponding to each scanned object in each floor.

[0020] C2. Compare the decoration quality assessment coefficient corresponding to each scanned object on each floor with the corresponding standard decoration quality assessment coefficient in the database. If the decoration quality assessment coefficient corresponding to a scanned object on a certain floor is smaller than the corresponding standard decoration quality assessment coefficient in the database, it is determined that the scanned object on this floor needs early warning feedback. Conversely, if the decoration quality assessment coefficient corresponding to a scanned object on a certain floor is greater than or equal to the corresponding standard decoration quality assessment coefficient in the database, it is determined that no early warning feedback is needed for the scanned object on this floor. In this way, it is determined whether early warning feedback is needed for each scanned object on each floor.

[0021] Preferably, the scanned object on the floor is subjected to quality warning feedback processing, and the specific feedback process is as follows: when the decoration quality assessment coefficient of a scanned object on a certain floor is lower than the standard decoration quality assessment coefficient, the warning mechanism is triggered. At this time, the robot dog will mark the object as "needing warning feedback". The robot dog will send the warning feedback information to the designated mobile device through the wireless network. The notification content includes: object type, floor where the object is located, decoration quality assessment coefficient and warning status that needs to be processed.

[0022] Preferably, the evaluation determines whether the overall construction quality of each floor in the target acceptance building meets the standards, and the specific analysis process is as follows: D1. After the robot dog completes the acceptance in the target acceptance building, the decoration quality evaluation coefficient corresponding to each type of scanning object in each floor of the target acceptance building is counted, and the decoration quality evaluation coefficient corresponding to each type of scanning object in each floor of the target acceptance building is accumulated, so as to obtain the accumulated value of the decoration quality evaluation coefficient corresponding to each type of scanning object in each floor of the target acceptance building.

[0023] D2. Compare the cumulative value of the decoration quality assessment coefficient corresponding to each type of scanning object in each floor of the target acceptance building with the cumulative value of the decoration quality assessment coefficient corresponding to the set corresponding type of scanning object. If the cumulative value of the decoration quality assessment coefficient corresponding to a certain type of scanning object in a certain floor of the target acceptance building is less than the cumulative value of the decoration quality assessment coefficient corresponding to the set standard corresponding type of scanning object, then the overall construction quality corresponding to the floor in the target acceptance building is not up to standard. If the cumulative value of the decoration quality assessment coefficient corresponding to a certain type of scanning object in a certain floor of the target acceptance building is greater than or equal to the cumulative value of the decoration quality assessment coefficient corresponding to the set standard corresponding type of scanning object, then the overall construction quality corresponding to the floor in the target acceptance building is up to standard.

[0024] Preferably, the acceptance results corresponding to each floor in the target acceptance building are evaluated and fed back, and the specific feedback process is as follows: F1. If the overall construction quality corresponding to a floor in the target acceptance building does not meet the standard, the robot dog will mark the floor as a "floor requiring rectification" and send the warning feedback information to the designated mobile device through the wireless network.

[0025] F2. If the overall construction quality of a floor in the building meets the standard, the robot dog will mark the floor as a "qualified floor" and send the early warning feedback information to the designated mobile device through the wireless network.

[0026] Preferably, the database is used to store the scanned object type corresponding to each standard decoration quality assessment coefficient.

[0027] The beneficial effects of the present invention are: 1. The embodiment of the present invention significantly reduces the time and manpower cost of manual inspection through the autonomous inspection of the robot dog, and improves the overall acceptance efficiency. The system can collect and analyze data in real time, quickly generate detection results, and avoid delays in traditional methods. By using AI technology and image recognition, the system eliminates the interference of human factors, ensures the objectivity and consistency of the acceptance results, and accurately identifies and evaluates the quality of decoration through high-precision sensors and algorithms, reducing the risk of missed detection and misjudgment.

[0028] 2. In the embodiment of the present invention, the decoration quality acceptance module can judge and feedback quality problems in real time, quickly notify the construction team to make rectifications, and improve the response speed. The system sets standards and compares actual data to intelligently judge whether it is necessary to issue an early warning, thereby reducing errors in manual judgment. A comprehensive quality assessment is conducted on each area of ​​the building, covering multiple dimensions such as size, flatness, and material ratio, to ensure the comprehensiveness of the quality assessment. By setting standards and assessment coefficients, a systematic quality management process is established, which helps to improve the overall project quality.

[0029] 3. In the embodiment of the present invention, through the automated process, acceptance personnel can focus more on important decision-making and management, and improve work efficiency. The application of this system is in line with the development direction of architecture and smart cities, and promotes the construction industry to develop in a more efficient and intelligent direction. The acceptance system using advanced technology helps to enhance the competitiveness of construction units in the market and meet customers' demand for high-quality decoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Embodiments of the present invention Figure 1 As shown, a decoration project inspection system based on a bionic robot dog includes: an acceptance preparation module, a decoration quality acceptance module, an overall acceptance evaluation module and a database.

[0034] The decoration quality acceptance module is connected to the acceptance preparation module and the overall acceptance evaluation module respectively, and the database is connected to the decoration quality acceptance module.

[0035] Acceptance preparation module: used to place the robot dog in the target acceptance building, prepare the robot dog, and plan the route of the robot dog after the preparation work is completed.

[0036] In a specific embodiment, the robot dog is prepared, and the specific preparation process is as follows: A1. Inspection preparation: adjust the robot dog to a squatting position and place it in the plane area of ​​the target acceptance building, and the ground in the plane area is flat. At the same time, there are no obstacles within 1 meter around the robot dog. Check whether the robot dog’s battery indicator light is on and confirm that it has sufficient power. Make sure that the battery is correctly installed in the battery slot without looseness or poor contact. If the battery is insufficient, charge or replace the battery in time. After confirming that all the above checks are correct, lightly press the robot dog’s power switch to turn on the robot dog’s power system.

[0037] It should be noted that within a range of 1 meter around the robot dog, ensure that there are no obstacles that affect the robot dog's standing up and movement, avoid accidental obstructions to the robot dog when starting, and ensure the smoothness of the movement.

[0038] A2. Exercise preparation: Use a mobile device to connect to the robot dog's WiFi network, ensure a stable connection, open the software interface, access the robot dog's control system, remotely access the robot dog's perception host through the connected device, select the exercise mode and command in the user interface, set the robot dog's action parameters, and after confirming that the set exercise command is correct, click the send button, the command will be transmitted to the robot dog in real time, and then the robot dog will start to move according to the received command, and adjust the direction and speed as needed.

[0039] A3. Obstacle avoidance preparation: The built-in ultrasonic radar of the robot dog starts working, scanning the environment ahead in real time to determine whether there are any obstacles. The robot dog will periodically send and receive ultrasonic signals to measure the distance to the obstacle ahead. Combined with the robot dog's built-in algorithm, the detected data is analyzed. If an obstacle appears ahead, the robot dog will quickly calculate the best obstacle avoidance path to ensure its safety. After confirming that there is an obstacle ahead, the robot dog will actively adjust its direction of travel and accelerate or decelerate.

[0040] It should be noted that for the description of the robot dog's built-in algorithm, the following is a detailed description of its technical implementation and workflow: 1. Working principle of ultrasonic radar, description: The robot dog's built-in ultrasonic radar scans its front environment in real time by transmitting and receiving ultrasonic signals. The specific workflow is as follows: Transmit signal: The robot dog's ultrasonic sensor sends out ultrasonic pulses. Receiving echoes: When the sound wave encounters an obstacle, it will be reflected back and the sensor receives these echo signals. Distance calculation: By measuring the time interval between signal transmission and reception, the robot dog can calculate the distance to the obstacle based on the propagation speed of the sound wave in the air (about 343 meters / second). 2. Data processing and obstacle identification, description: The ultrasonic echo signal received by the robot dog is processed by the built-in algorithm. The main steps include: Data filtering: Noise suppression and filtering of the received signal to improve the accuracy of the measurement. Real-time analysis: Detect the distance data to the obstacle and determine whether it is within the set threshold to determine whether there is an obstacle in front. Environmental modeling: Combine the data of multiple sensors, such as gyroscopes, accelerometers, etc., to build a dynamic model of the surrounding environment and identify the shape, size and position of the obstacle. 3. Automatic avoidance algorithm, Description: Once it determines that there is an obstacle ahead, the robot dog's built-in intelligent algorithm will react quickly. The main algorithm process includes: Path planning: Using algorithms such as Dijkstra algorithm or deep reinforcement learning, calculate the best obstacle avoidance path, taking into account the current direction of travel and the location of the obstacle. Decision making: After calculating the potential avoidance path, the algorithm will select an optimal path to minimize the travel distance and time while ensuring safety. 4. Travel direction adjustment and speed control, Description: After the robot dog confirms the existence of an obstacle ahead, it will take the following actions to optimize the travel path: Direction adjustment: The robot dog adjusts the travel direction by controlling the servo and motor. The algorithm calculates the new driving angle and determines the best steering amplitude and timing through a feedback mechanism. Acceleration and deceleration: The robot dog can adjust its movement speed according to the distance of the obstacle and its own speed: Deceleration: When approaching an obstacle, the motor power is reduced so that the robot dog approaches at a lower speed, thereby ensuring more accurate avoidance. Acceleration: Once the obstacle is successfully avoided, the robot dog can gradually accelerate and return to normal driving speed. 5. Real-time feedback and adjustment loop,Description: The robot dog will continuously monitor its environment during driving to ensure the effectiveness of the obstacle avoidance strategy: Continuous scanning: The robot dog will regularly send and receive ultrasonic signals and update the environmental data in real time. Dynamic adjustment: If a new obstacle appears on the adjusted path, the robot dog will re-plan the path and adjust the direction and speed in time, forming a closed-loop feedback control system.

[0041] In another specific embodiment, the robot dog that has completed the preparation work plans a route, and the specific planning process is as follows: B1. When the power of the robot dog is turned on and is in standby mode, an initialization instruction is sent through the user interface to start the robot dog's system. During this process, the robot dog will perform a self-check to ensure that each sensor and system module is operating normally.

[0042] B2. The robot dog will start the lidar system to conduct a comprehensive environmental scan. The lidar emits laser beams through high-speed rotation to collect three-dimensional spatial data of the surrounding environment. This data will be transmitted back to the internal processing system in real time. The robot dog will automatically start the point cloud mapping program to convert the scanned laser data into point cloud data.

[0043] B3. After the point cloud data is processed, the robot dog will generate a detailed point cloud map, which accurately reflects the geometric shapes and spatial relationships of the surrounding buildings. The robot dog converts the point cloud map into a raster map, where the raster map divides the environment into regular grids to facilitate subsequent algorithms for path planning and positioning. Each grid represents whether there are passable or impassable obstacles in the unit space.

[0044] B4. Using the HDL-localization positioning algorithm, the robot dog will accurately locate itself based on the grid map and sensor data. The robot dog can update its position and posture in the environment in real time to ensure highly accurate feedback on its own dynamic state. Based on the determined starting point, end point and environmental information, the robot dog will automatically plan the inspection path and generate an optimized inspection route.

[0045] Decoration quality acceptance module: When the robot dog is conducting acceptance processing on each floor of the target acceptance building, it will determine whether each scanned object on each floor needs early warning feedback. If a scanned object on a certain floor needs early warning feedback, the scanned object on that floor will be subjected to quality early warning feedback.

[0046] In a specific embodiment, the decoration quality acceptance module also includes a data acquisition unit and a data analysis unit, wherein the data acquisition unit is used to collect basic information corresponding to each scanned object on each floor, and the basic information includes size, flatness, material ratio and distance corresponding to each other decoration material.

[0047] The data analysis unit is used to analyze and obtain the decoration quality evaluation coefficient corresponding to each scanned object on each floor according to the basic information corresponding to each scanned object on each floor.

[0048] It should be noted that the length, width and height of an object can be measured quickly and accurately using the robot dog's built-in laser rangefinder or 3D laser scanner. The device can generate three-dimensional point cloud data to extract the specific size information of the object, and use the robot dog's built-in level or level (such as an electronic level) to measure the flatness of the ground or wall. The flatness is obtained by comparing the height difference between the horizontal rod or ruler and the measuring surface. The wall and ground materials are tested using the robot dog's built-in portable material testing instrument to investigate the density, strength, etc. of the materials. These data can be combined with document information to deduce the material ratio. After a spatial scan, the generated 3D model can be used to measure the distance between different decoration materials. The standard distance between materials can be accurately measured through software tools.

[0049] In another specific embodiment, the analysis obtains the decoration quality evaluation coefficient corresponding to each scanned object on each floor of the target acceptance building. The specific analysis process is as follows: the size, flatness, proportion of each material and distance from other decoration materials corresponding to each scanned object on each floor are recorded as , , and ,in, Indicates the number corresponding to each floor. , u is any integer greater than 2, Indicates the number corresponding to each scanned object, , n is any integer greater than 2, Indicates the number corresponding to each material, , m is any integer greater than 2, Indicates the corresponding numbers of other decoration materials. , v is any integer greater than 2, substitute it into the calculation formula The decoration quality evaluation coefficient corresponding to each scanned object on each floor is obtained ,in, , , , They are the standard size, standard flatness, standard ratio of materials, and standard distance from other decoration materials corresponding to the set scanning object. , , , They are respectively the weight factor corresponding to the set scanned object size, the weight factor corresponding to the flatness, the weight factor corresponding to the material ratio, and the weight factor corresponding to the distance from other decoration materials.

[0050] It should be noted that , , , Both are greater than 0 and less than 1.

[0051] It should also be noted that through the summary of a large amount of research data and experimental data, the standard size, standard flatness, standard ratio of materials, and standard distance from other decoration materials corresponding to the scanned object are set by professional institutions and research institutions. At the same time, based on the professional knowledge and research basis of experts in the field, and discussed and confirmed with industry organizations or professional institutions, the experts set the weight factors corresponding to the size of the scanned object, the weight factors corresponding to the flatness, the weight factors corresponding to the material ratio, and the weight factors corresponding to the distance from other decoration materials based on their own experience and knowledge.

[0052] It needs to be explained again that the standard size, standard flatness, standard ratio of materials, and standard distance corresponding to other decoration materials are the size threshold, flatness threshold, ratio threshold corresponding to materials, and distance threshold corresponding to other decoration materials, respectively. For example: the standard size is 24, the standard flatness is 2, the standard ratio of materials is 1.5, and the standard distance corresponding to other decoration materials is 15.

[0053] In another specific embodiment, the judgment of whether each scanned object in each floor needs early warning feedback is as follows: C1. The robot dog uses YOLOv8 cutting vision to identify the type of each scanned object in each floor, and compares the type of each scanned object in each floor with the scanned object type corresponding to each standard decoration quality assessment coefficient in the database, and then obtains the standard decoration quality assessment coefficient corresponding to each scanned object in each floor.

[0054] C2. Compare the decoration quality assessment coefficient corresponding to each scanned object on each floor with the corresponding standard decoration quality assessment coefficient in the database. If the decoration quality assessment coefficient corresponding to a scanned object on a certain floor is smaller than the corresponding standard decoration quality assessment coefficient in the database, it is determined that the scanned object on this floor needs early warning feedback. Conversely, if the decoration quality assessment coefficient corresponding to a scanned object on a certain floor is greater than or equal to the corresponding standard decoration quality assessment coefficient in the database, it is determined that no early warning feedback is needed for the scanned object on this floor. In this way, it is determined whether early warning feedback is needed for each scanned object on each floor.

[0055] The embodiments of the present invention significantly reduce the time and manpower cost of manual inspections through the autonomous inspections of the robot dog, and improve the overall acceptance efficiency. The system can collect and analyze data in real time, quickly generate detection results, and avoid delays in traditional methods. By using AI technology and image recognition, the system eliminates the interference of human factors, ensures the objectivity and consistency of the acceptance results, and accurately identifies and evaluates the quality of decoration through high-precision sensors and algorithms, reducing the risks of missed detection and misjudgment.

[0056] In a specific embodiment, the scanned object on the floor is subjected to quality warning feedback processing, and the specific feedback process is as follows: when the decoration quality assessment coefficient of a scanned object on a certain floor is lower than the standard decoration quality assessment coefficient, the warning mechanism is triggered. At this time, the robot dog will mark the object as "needing warning feedback". The robot dog will send the warning feedback information to the designated mobile device through the wireless network. The notification content includes: object type, floor where the object is located, decoration quality assessment coefficient and warning status that needs to be processed.

[0057] In the embodiment of the present invention, the decoration quality acceptance module can judge and feedback quality problems in real time, quickly notify the construction team to make rectifications, and improve the response speed. The system sets standards and compares actual data to intelligently judge whether it is necessary to issue an early warning, thereby reducing errors in manual judgment. A comprehensive quality assessment is conducted on various areas of the building, covering multiple dimensions such as size, flatness, and material ratio, to ensure the comprehensiveness of the quality assessment. By setting standards and assessment coefficients, a systematic quality management process is established, which helps to improve the overall project quality.

[0058] Overall acceptance evaluation module: used to evaluate whether the overall construction quality of each floor in the target acceptance building meets the standards, and to provide evaluation feedback on the acceptance results of each floor in the target acceptance building.

[0059] In a specific embodiment, the evaluation determines whether the overall construction quality of each floor in the target acceptance building meets the standards, and the specific analysis process is as follows: D1. After the robot dog completes the acceptance in the target acceptance building, the decoration quality evaluation coefficient corresponding to each type of scanning object in each floor of the target acceptance building is counted, and the decoration quality evaluation coefficient corresponding to each type of scanning object in each floor of the target acceptance building is accumulated, so as to obtain the accumulated value of the decoration quality evaluation coefficient corresponding to each type of scanning object in each floor of the target acceptance building.

[0060] D2. Compare the cumulative value of the decoration quality assessment coefficient corresponding to each type of scanning object in each floor of the target acceptance building with the cumulative value of the decoration quality assessment coefficient corresponding to the set corresponding type of scanning object. If the cumulative value of the decoration quality assessment coefficient corresponding to a certain type of scanning object in a certain floor of the target acceptance building is less than the cumulative value of the decoration quality assessment coefficient corresponding to the set standard corresponding type of scanning object, then the overall construction quality corresponding to the floor in the target acceptance building is not up to standard. If the cumulative value of the decoration quality assessment coefficient corresponding to a certain type of scanning object in a certain floor of the target acceptance building is greater than or equal to the cumulative value of the decoration quality assessment coefficient corresponding to the set standard corresponding type of scanning object, then the overall construction quality corresponding to the floor in the target acceptance building is up to standard.

[0061] In another specific embodiment, the acceptance results corresponding to each floor in the target acceptance building are evaluated and fed back, and the specific feedback process is as follows: F1. If the overall construction quality corresponding to a floor in the target acceptance building does not meet the standard, the robot dog will mark the floor as a "floor requiring rectification" and send the warning feedback information to the designated mobile device through the wireless network.

[0062] F2. If the overall construction quality of a floor in the building meets the standard, the robot dog will mark the floor as a "qualified floor" and send the early warning feedback information to the designated mobile device through the wireless network.

[0063] The database is used to store the scanned object types corresponding to each standard decoration quality assessment coefficient.

[0064] The embodiments of the present invention enable acceptance personnel to focus more on important decision-making and management through automated processes, thereby improving work efficiency. The application of this system is in line with the development direction of architecture and smart cities, and promotes the construction industry to develop in a more efficient and intelligent direction. The acceptance system using advanced technology helps to enhance the competitiveness of construction units in the market and meet customer demand for high-quality decoration.

[0065] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A decoration engineering inspection system based on a bionic robot dog, characterized in that: include: Acceptance preparation module: used to place the robot dog in the target acceptance building, prepare the robot dog, and plan the route of the robot dog after the preparation work is completed; Decoration quality acceptance module: when the robot dog is conducting acceptance processing on each floor of the target acceptance building, it determines whether each scanned object on each floor needs to be given a warning feedback. If a scanned object on a certain floor needs a warning feedback, the scanned object on that floor will be given a quality warning feedback processing. Overall acceptance evaluation module: used to evaluate whether the overall construction quality of each floor in the target acceptance building meets the standards, and to provide evaluation feedback on the acceptance results of each floor in the target acceptance building.

2. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 1, characterized in that: The robot dog is prepared, and the specific preparation process is as follows: A1. Preparation for inspection: adjust the robot dog to a squatting position and place it in the plane area of ​​the target building for acceptance. The ground in the plane area should be flat. At the same time, there should be no obstacles within 1 meter around the robot dog. Check whether the robot dog’s battery indicator light is on and confirm that it has sufficient power. Make sure that the battery is correctly installed in the battery slot and is not loose or in poor contact. If the battery is insufficient, charge or replace the battery in time. After confirming that all the above checks are correct, lightly press the robot dog’s power switch to turn on the robot dog’s power system. A2. Movement preparation: Use a mobile device to connect to the robot dog's WiFi network, ensure a stable connection, open the software interface, access the robot dog's control system, remotely access the robot dog's perception host through the connected device, select the movement mode and command in the user interface, set the robot dog's action parameters, and after confirming that the set movement command is correct, click the send button, and the command will be transmitted to the robot dog in real time. Subsequently, the robot dog will start to move according to the received command, and adjust the direction and speed as needed; A3. Obstacle avoidance preparation: The built-in ultrasonic radar of the robot dog starts working, scanning the environment ahead in real time to determine whether there are any obstacles. The robot dog will periodically send and receive ultrasonic signals to measure the distance to the obstacle ahead. Combined with the robot dog's built-in algorithm, the detected data is analyzed. If an obstacle appears ahead, the robot dog will quickly calculate the best obstacle avoidance path to ensure its safety. After confirming that there is an obstacle ahead, the robot dog will actively adjust its direction of travel and accelerate or decelerate.

3. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 1, characterized in that: The robot dog that has completed the preparation work will plan its route. The specific planning process is as follows: B1. When the robot dog is powered on and in standby mode, send an initialization command through the user interface to start the robot dog's system. During this process, the robot dog will perform a self-check to ensure that each sensor and system module is operating normally; B2. The robot dog will start the laser radar system to conduct a comprehensive environmental scan. The laser radar emits laser beams through high-speed rotation to collect three-dimensional spatial data of the surrounding environment. This data will be transmitted back to the internal processing system in real time. The robot dog will automatically start the point cloud mapping program to convert the scanned laser data into point cloud data. B3. After the point cloud data is processed, the robot dog will generate a detailed point cloud map, which accurately reflects the geometric shape and spatial relationship of the surrounding buildings. The robot dog converts the point cloud map into a grid map, where the grid map divides the environment into regular grids to facilitate the subsequent algorithm to perform path planning and positioning. Each grid represents whether there are passable or impassable obstacles in the unit space; B4. Using the HDL-localization positioning algorithm, the robot dog will accurately locate itself based on the grid map and sensor data. The robot dog can update its position and posture in the environment in real time to ensure highly accurate feedback on its own dynamic state. Based on the determined starting point, end point and environmental information, the robot dog will automatically plan the inspection path and generate an optimized inspection route.

4. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 1, characterized in that: The decoration quality acceptance module also includes a data acquisition unit and a data analysis unit. The data acquisition unit is used to collect basic information corresponding to each scanned object on each floor. The basic information includes size, flatness, ratio of each material and distance corresponding to each other decoration material; The data analysis unit is used to analyze and obtain the decoration quality evaluation coefficient corresponding to each scanned object on each floor according to the basic information corresponding to each scanned object on each floor.

5. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 4, characterized in that: The analysis obtains the decoration quality assessment coefficient corresponding to each scanned object on each floor of the target acceptance building. The specific analysis process is as follows: The size, flatness, proportion of each material and distance from other decoration materials of each scanned object on each floor are recorded as , , and ,in, Indicates the number corresponding to each floor. , u is any integer greater than 2, Indicates the number corresponding to each scanned object, , n is any integer greater than 2, Indicates the number corresponding to each material, , m is any integer greater than 2, Indicates the corresponding numbers of other decoration materials. , v is any integer greater than 2, substitute it into the calculation formula The decoration quality evaluation coefficient corresponding to each scanned object on each floor is obtained ,in, , , , They are the standard size, standard flatness, standard ratio of materials, and standard distance from other decoration materials corresponding to the set scanning object. , , , They are respectively the weight factor corresponding to the set scanned object size, the weight factor corresponding to the flatness, the weight factor corresponding to the material ratio, and the weight factor corresponding to the distance from other decoration materials.

6. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 5, characterized in that: The specific judgment process of judging whether each scanned object on each floor needs to be given an early warning feedback is as follows: C1. The robot dog uses YOLOv8 cutting vision to identify the type of each scanned object on each floor, and compares the type of each scanned object on each floor with the type of scanned object corresponding to each standard decoration quality assessment coefficient in the database, and then obtains the standard decoration quality assessment coefficient corresponding to each scanned object on each floor; C2. Compare the decoration quality assessment coefficient corresponding to each scanned object on each floor with the corresponding standard decoration quality assessment coefficient in the database. If the decoration quality assessment coefficient corresponding to a scanned object on a certain floor is smaller than the corresponding standard decoration quality assessment coefficient in the database, it is determined that the scanned object on this floor needs early warning feedback. Conversely, if the decoration quality assessment coefficient corresponding to a scanned object on a certain floor is greater than or equal to the corresponding standard decoration quality assessment coefficient in the database, it is determined that no early warning feedback is needed for the scanned object on this floor. In this way, it is determined whether early warning feedback is needed for each scanned object on each floor.

7. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 6, characterized in that: The quality warning feedback processing of the scanned object on the floor is performed, and the specific feedback process is as follows: When the decoration quality assessment coefficient of a scanned object on a certain floor is lower than the standard decoration quality assessment coefficient, the early warning mechanism is triggered. At this time, the robot dog will mark the object as "requiring early warning feedback". The robot dog will send the early warning feedback information to the designated mobile device through the wireless network. The notification content includes: object type, floor where the object is located, decoration quality assessment coefficient and early warning status that needs to be processed.

8. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 1, characterized in that: The assessment target is to check whether the overall construction quality of each floor in the building meets the standards. The specific analysis process is as follows: D1. When the robot dog completes the acceptance in the target acceptance building, it counts the decoration quality assessment coefficients corresponding to each type of scanned object in each floor of the target acceptance building, and accumulates the decoration quality assessment coefficients corresponding to each type of scanned object in each floor of the target acceptance building, thereby obtaining the accumulated value of the decoration quality assessment coefficients corresponding to each type of scanned object in each floor of the target acceptance building; D2. Compare the cumulative value of the decoration quality assessment coefficient corresponding to each type of scanning object in each floor of the target acceptance building with the cumulative value of the decoration quality assessment coefficient corresponding to the set corresponding type of scanning object. If the cumulative value of the decoration quality assessment coefficient corresponding to a certain type of scanning object in a certain floor of the target acceptance building is less than the cumulative value of the decoration quality assessment coefficient corresponding to the set standard corresponding type of scanning object, then the overall construction quality corresponding to the floor in the target acceptance building is not up to standard. If the cumulative value of the decoration quality assessment coefficient corresponding to a certain type of scanning object in a certain floor of the target acceptance building is greater than or equal to the cumulative value of the decoration quality assessment coefficient corresponding to the set standard corresponding type of scanning object, then the overall construction quality corresponding to the floor in the target acceptance building is up to standard.

9. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 8, characterized in that: The acceptance results corresponding to each floor in the target acceptance building are evaluated and fed back. The specific feedback process is as follows: F1. If the overall construction quality of a floor in the building under acceptance does not meet the standard, the robot dog will mark the floor as "revision required" and send the early warning feedback information to the designated mobile device through the wireless network; F2. If the overall construction quality of a floor in the building meets the standard, the robot dog will mark the floor as a "qualified floor" and send the early warning feedback information to the designated mobile device through the wireless network.

10. A decoration engineering inspection system based on a bionic robot dog as claimed in claim 1, characterized in that: It also includes a database, which is used to store the scanned object type corresponding to each standard decoration quality assessment coefficient.

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