A decoration engineering inspection system based on a bionic robot dog

Abstract

The application discloses a kind of based on bionic robot dog's decoration engineering inspection system, it is related to decoration engineering inspection technical field, by the autonomous inspection of robot dog, significantly reduce the time and manpower cost of artificial inspection, improve overall acceptance efficiency, system can real-time acquisition and analysis data, quickly generate detection result, avoid the delay in traditional method, using AI technology and image recognition, system eliminates the interference of human factor, ensure the objectivity and consistency of acceptance result, by high-precision sensor and algorithm, accurately identify and evaluate decoration quality, reduce the risk of missed detection and misjudgment, through automated process, acceptance personnel can concentrate more energy on important decision and management, improve work efficiency, the application of the system meets the development direction of building and smart city, promote the development of building industry to more efficient, more intelligent direction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of decoration engineering inspection, and particularly relates to a decoration engineering inspection system based on a bionic robot dog. BACKGROUND

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

[0003] Prior art such as the invention patent application with publication number CN114745399A discloses an intelligent building engineering quality acceptance management system. The image recognition detection module can accurately identify the area to be inspected from the inspection image, and detect the area to be inspected based on the identification result to generate detection data. The data comparison module obtains the corresponding relevant acceptance rules from the acceptance rule generation module based on the identification result, and compares and analyzes the detection data and the relevant acceptance rules. The acceptance information generation module generates relevant acceptance information, and the user network node authenticates the acceptance information, achieving comprehensive evaluation of the quality of the building engineering while ensuring the accuracy of the final acceptance result of the acceptance project. The technical solution provided by the present application can effectively overcome the defects of the prior art, such as poor accuracy of the acceptance result and inability to comprehensively evaluate the quality of the building engineering.

[0004] For the above-mentioned solution, the present application applicant found that the above-mentioned technology at least has 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 actual building decoration acceptance, the number of inspection lots is large, the material technology updates rapidly, and the personnel acceptance is difficult and inefficient. Manual inspection usually takes a long time to complete, especially in large-scale construction projects, the efficiency is low, and it cannot meet the demand of rapid acceptance. Long-term manual inspection can easily lead to personnel fatigue and increase the risk of errors in inspection.

[0005] 2. The traditional method is difficult to comprehensively evaluate each link of the building engineering, often only focuses on surface problems, ignores potential deep-seated quality problems, lacks systematic evaluation process and standard, and leads to difficulty in effectively comparing and analyzing the acceptance results of different projects. At the same time, it is often difficult to quickly adapt to changes in new materials and construction technology, affecting the acceptance standard and effect. The experience and judgment standard of each acceptance personnel is different, leading to lack of standardization and consistency of the acceptance result. SUMMARY

[0006] In view of the above technical deficiencies, the present application aims to provide a renovation project inspection system based on a bionic robot dog.

[0007] To solve the above technical problems, the present application adopts the following technical solutions: The present application provides a renovation project inspection system based on a bionic robot dog, comprising: an acceptance preparation module: for placing a robot dog in a target acceptance building and preparing the robot dog, and planning a route for the robot dog after the preparation is completed.

[0008] A renovation quality acceptance module: for determining whether a scanned object in each floor needs to be prewarned and fed back when the robot dog performs acceptance processing in each floor of the target acceptance building, and performing quality prewarning and feedback processing on the scanned object in the floor if the scanned object needs to be prewarned and fed back.

[0009] A whole acceptance evaluation module: for evaluating whether the whole construction quality of each floor in the target acceptance building meets the standard, and evaluating and feeding back the acceptance results of each floor in the target acceptance building.

[0010] Preferably, the robot dog is prepared as follows: A1, detection preparation: adjust the robot dog to a squatting posture, place it in the target acceptance building flat area, and the flat area ground is flat, at the same time, there is no obstacle within 1 meter around the robot dog, check whether the robot dog battery indicator light is on, and confirm that the battery is sufficient, ensure 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, and after confirming that all the above checks are correct, press the power switch of the robot dog to turn on the power system of the robot dog.

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

[0012] A3, obstacle avoidance preparation: the ultrasonic radar built in the robot dog starts to work, scans the environment in front in real time, judges whether there is an obstacle, the robot dog will periodically send and receive ultrasonic signals, measure the distance between the robot dog and the obstacle in front, combine the built-in algorithm of the robot dog, analyze the detected data, if there is an obstacle in front, the robot dog will quickly calculate the best obstacle avoidance path to ensure the safety of its travel, after confirming that there is an obstacle in front, the robot dog will actively adjust its travel direction and speed up or slow down.

[0013] Preferably, the robot dog with the completed preparation work plans the route, and the specific planning process is as follows: B1, when the power of the robot dog is turned on and in standby state, send initialization instruction through user interface, start the system of the robot dog, in this process, the robot dog will perform self-checking to ensure that each sensor and system module is operating normally.

[0014] B2, the robot dog will start the laser radar system to conduct comprehensive environmental scanning, the laser radar transmits laser beams by high-speed rotation to collect three-dimensional space data of the surrounding environment, which will be transmitted back to the internal processing system in real time, the robot dog automatically starts 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 shape and spatial relationship of the surrounding buildings, the robot dog converts the point cloud map into a grid map, wherein the grid map divides the environment into regular grids to facilitate subsequent algorithm for path planning and positioning, each grid represents whether there is a passable or impassable obstacle in the unit space.

[0016] B4, using the hdl-localization positioning algorithm, the robot dog will be accurately positioned based on the grid map and sensor data, the robot dog can update its position and attitude in the environment in real time to ensure highly accurate feedback of its 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 further comprises a data acquisition unit and a data analysis unit, the data acquisition unit is used to acquire basic information corresponding to each scanned object in each floor, the basic information includes size, flatness, material ratio and distance corresponding to each other decoration material.

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

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

[0020] C2, compare the decoration quality evaluation coefficient of each scanned object in each floor with the corresponding standard decoration quality evaluation coefficient in the database. If the decoration quality evaluation coefficient of a scanned object in a floor is less than the corresponding standard decoration quality evaluation coefficient in the database, it is determined that the scanned object in the floor needs early warning feedback, otherwise, if the decoration quality evaluation coefficient of a scanned object in a floor is greater than or equal to the corresponding standard decoration quality evaluation coefficient in the database, it is determined that the scanned object in the floor does not need early warning feedback. In this way, whether each scanned object in each floor needs early warning feedback is determined.

[0021] Preferably, the quality early warning feedback processing of the scanned object in the floor is as follows: when the decoration quality evaluation coefficient of a scanned object in a floor is lower than the standard decoration quality evaluation coefficient, the early warning mechanism is triggered, at this time, the robot dog marks the object as "early warning feedback required", and sends the early warning feedback information to the designated mobile device through the wireless network, the notification content includes: object type, object floor, decoration quality evaluation coefficient and early warning state required to handle.

[0022] Preferably, the evaluation of whether the overall construction quality of each floor in the target acceptance building meets the standard is as follows: D1, when the robot dog completes the acceptance in the target acceptance building, the decoration quality evaluation coefficient of each scanned object in each type of scanned object in each floor in the target acceptance building is calculated, and the decoration quality evaluation coefficient of each scanned object in each type of scanned object in each floor in the target acceptance building is accumulated, and then the decoration quality evaluation coefficient cumulative value of each type of scanned object in each floor in the target acceptance building is obtained.

[0023] D2, compare the cumulative value of the decoration quality evaluation coefficient corresponding to each type of scanned object in each floor of the target acceptance building with the set cumulative value of the decoration quality evaluation coefficient corresponding to the corresponding type of scanned object, if the cumulative value of the decoration quality evaluation coefficient corresponding to a type of scanned object in a floor of the target acceptance building is less than the set cumulative value of the decoration quality evaluation coefficient corresponding to the corresponding type of scanned object, the overall construction quality of the corresponding floor of the target acceptance building is evaluated as substandard, if the cumulative value of the decoration quality evaluation coefficient corresponding to a type of scanned object in a floor of the target acceptance building is greater than or equal to the set cumulative value of the decoration quality evaluation coefficient corresponding to the corresponding type of scanned object, the overall construction quality of the corresponding floor of the target acceptance building is evaluated as standard.

[0024] Preferably, the acceptance results corresponding to each floor of the target acceptance building are evaluated and fed back, and the specific feedback process is as follows: F1, if the overall construction quality of a floor of the target acceptance building is substandard, the robot dog will mark the floor as "floor needing rectification", and the robot dog will send early warning feedback information to the designated mobile device through the wireless network.

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

[0026] Preferably, the database is used to store the types of scanned objects corresponding to each standard decoration quality evaluation coefficient.

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

[0028] 2. The embodiment of the present application can judge and feedback quality problems in real time through the decoration quality acceptance module, quickly notify the construction team to rectify, improve the response speed, the system compares the set standard with the actual data, intelligently judges whether to issue a warning, reduces the errors of manual judgment, comprehensively evaluates the quality of each area of the building, covers multiple dimensions such as size, flatness and material ratio, ensures the comprehensiveness of quality evaluation, sets standards and evaluation coefficients, establishes a systematic quality management process, and helps to improve the overall engineering quality.

[0029] 3、The embodiment of the present application, through the automatic process, the acceptance personnel can concentrate more energy on important decisions and management, improve work efficiency, the application of the system meets the development direction of building and smart city, promotes the building industry to more efficient, more intelligent direction, the acceptance system using advanced technology helps to improve the competitiveness of construction units in the market, meet the needs of customers for high-quality decoration. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0031] Figure 1 The system module connection diagram of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0033] The embodiment of the present application comprises Figure 1 As shown in the figure, a decoration engineering inspection system based on a bionic robot dog comprises 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 with the acceptance preparation module and the overall acceptance evaluation module respectively, and the database is connected with the decoration quality acceptance module.

[0035] The acceptance preparation module is used for placing a robot dog in a target acceptance building and preparing the robot dog, and planning a route for the robot dog after the preparation is completed.

[0036] In a specific embodiment, the preparation of the robot dog is as follows: A1, detection of preparation: adjust the robot dog to a squatting posture, place it in the target acceptance building plane area, and the ground in the plane area is flat, at the same time, there is no obstacle within 1 meter around the robot dog, check whether the battery indicator light of the robot dog is on, and confirm that the battery is sufficient, ensure 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, press the power switch of the robot dog to turn on the power system of the robot dog.

[0037] It should be noted that the range of 1 meter around the robot dog ensures that there is no obstacle affecting the robot dog's getting up and movement, avoiding unexpected obstacles when starting the robot dog, and ensuring the smoothness of the movement.

[0038] A2, movement preparation: use a mobile device to connect the robot dog's WiFi network, ensure 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 instructions in the user interface, set the robot dog's action parameters, after confirming that the set movement instructions are correct, click the send button, the instructions will be transmitted to the robot dog in real time, then the robot dog body will start moving according to the received instructions, and adjust the direction and speed as needed.

[0039] A3, obstacle avoidance preparation: the ultrasonic radar built-in the robot dog starts to work, scans the front environment in real time, judges whether there is an obstacle, the robot dog will periodically send and receive ultrasonic signals to measure the distance between the front obstacle, combined with the built-in algorithm of the robot dog, analyzes the detected data, if there is an obstacle in front, the robot dog will quickly calculate the best obstacle avoidance path to ensure its safety, after confirming that there is an obstacle in front, the robot dog will actively adjust its direction and speed up or slow down.

[0040] Note that for the description of the built-in algorithm of the robot dog, the following is a detailed description of its technical implementation and workflow: 1. Working principle of ultrasonic radar, description: The built-in ultrasonic radar of the robot dog scans its front environment in real time by emitting and receiving ultrasonic signals. The specific working process is as follows: Signal emission: The ultrasonic sensor of the robot dog emits ultrasonic pulses. Echo reception: 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 emission and reception, the robot dog can calculate the distance to the obstacle according to the propagation speed of sound waves in air (about 343 meters per second). 2. Data processing and obstacle identification, description: The ultrasonic echo signals received by the robot dog are processed by the built-in algorithm, the main steps include: Data filtering: The received signal is subjected to noise suppression and filtering to improve the accuracy of measurement. Real-time analysis: Detect the distance data of the obstacle and judge whether it is within the set threshold to determine whether there is an obstacle in front. Environment modeling: Combine the data of multiple sensors, such as gyroscopes, accelerometers, etc., to build a dynamic model of the surrounding environment, identify the shape, size and position of the obstacle. 3. Automatic avoidance algorithm, description: Once it is determined that there is an obstacle in front, the built-in intelligent algorithm of the robot dog will quickly respond, the main algorithm process includes: Path planning: Use algorithms such as Dijkstra algorithm or deep reinforcement learning to calculate the best obstacle avoidance path, considering the current travel direction and obstacle position. Decision making: After calculating the potential avoidance path, the algorithm will choose the optimal path to minimize travel distance and time while ensuring safety. 4. Travel direction adjustment and speed control, description: After the robot dog confirms the existence of the obstacle in front, it will take the following actions to optimize the travel path: Direction adjustment: The robot dog adjusts the travel direction by controlling the rudder and motor. The algorithm calculates the new travel angle and determines the optimal steering amplitude and steering timing through the feedback mechanism. Acceleration and deceleration: The robot dog can adjust the moving speed according to the distance of the obstacle and its own speed: Deceleration: When approaching the obstacle, reduce the power of the motor to make the robot dog approach at a lower speed, ensuring more accurate avoidance. Acceleration: Once the obstacle is successfully avoided, the robot dog can gradually accelerate to restore normal travel speed. 5. Real-time feedback and adjustment cycle, description: The robot dog will continuously monitor its environment during travel to ensure the effectiveness of the obstacle avoidance strategy: Continuous scanning: The robot dog will periodically send and receive ultrasonic signals and update environmental data in real time. Dynamic adjustment: If new obstacles appear on the adjusted path, the robot dog will re-plan the path and adjust the travel direction and speed in a timely manner, forming a closed-loop feedback control system.

[0041] In another specific embodiment, the machine dog that has completed the preparation work plans the route, and the specific planning process is as follows: B1, when the power supply of the machine dog has been turned on and is in a standby state, send an initialization instruction through the user interface to start the system of the machine dog. In this process, the machine dog will perform self-checking to ensure that each sensor and system module is operating normally.

[0042] B2, the machine dog will start the laser radar system and perform a comprehensive environmental scan. The laser radar emits a laser beam by high-speed rotation to collect three-dimensional spatial data of the surrounding environment. These data will be transmitted back to the internal processing system in real time, and the machine 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 machine dog will generate a detailed point cloud map that accurately reflects the geometric shape and spatial relationship of the surrounding buildings. The machine dog will convert the point cloud map into a grid map, where the grid map divides the environment into regular grids to facilitate subsequent algorithm path planning and positioning. Each grid represents whether there is a passable or impassable obstacle in the unit space.

[0044] B4, using the hdl-localization positioning algorithm, the machine dog will accurately position based on the grid map and sensor data. The machine dog can update its position and attitude in the environment in real time to ensure highly accurate feedback of its dynamic state. Based on the determined starting point, end point, and environmental information, the machine dog will automatically plan a patrol route and generate an optimized patrol route.

[0045] The decoration quality acceptance module is used to determine whether each scanned object in each floor needs to be prewarned when the machine dog is in the target acceptance building. If a scanned object in a floor needs to be prewarned, the quality prewarning feedback of the scanned object in the floor is processed.

[0046] In one specific embodiment, the decoration quality acceptance module further includes a data acquisition unit and a data analysis unit. The data acquisition unit is used to acquire basic information of each scanned object in each floor, including size, flatness, material ratio, and distance from other decoration materials.

[0047] The data analysis unit is used to analyze the decoration quality evaluation coefficient of each scanned object in each floor based on the basic information of each scanned object in each floor.

[0048] It should be noted that the built-in laser range finder or 3D laser scanner of the robot dog can quickly and accurately measure the length, width and height of the object. The device can generate three-dimensional point cloud data to extract the specific size information of the object, and use the built-in level or level (such as electronic level) of the robot dog to measure the flatness of the ground or wall. The flatness is obtained by comparing the height difference between the level rod or ruler and the measured surface, and the built-in portable material testing instrument of the robot dog is used to detect the wall and ground materials to investigate the density and strength of the materials. These data can be combined with the document materials to calculate the proportion of the materials. After scanning the space, the generated 3D model can be used to measure the distance between different decoration materials. Through software tools, the standard distance between materials can be accurately measured.

[0049] In another specific embodiment, the analysis obtains the decoration quality evaluation coefficient corresponding to each scanned object in each floor, and the specific analysis process is as follows: the size, flatness, proportion of each material, and distance corresponding to each other decoration material of each scanned object in each floor are respectively denoted as 、 、 and , wherein represents the number corresponding to each floor, , u is any integer greater than 2, represents the number corresponding to each scanned object, , n is any integer greater than 2, represents the number corresponding to each material, , m is any integer greater than 2, represents the number corresponding to each other decoration material, , v is any integer greater than 2, and the calculation formula is substituted to obtain the decoration quality evaluation coefficient corresponding to each scanned object in each floor, wherein 、 、 、 are the standard size, standard flatness, standard proportion of materials, and standard distance corresponding to other decoration materials of the scanned object, respectively, 、 、 、 are the weight factors corresponding to the size of the scanned object, the weight factors corresponding to the flatness, the weight factors corresponding to the proportion of materials, and the weight factors corresponding to the distance of other decoration materials, respectively.

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

[0051] It also needs to be explained that through a large amount of research data and experimental data summary. According to the professional institutions, research institutions set the corresponding standard size of the scanned object, the standard flatness, the corresponding standard proportion of the material, and the corresponding standard distance with other decoration materials, at the same time, according to the professional knowledge and research basis of the field experts, and discuss and confirm with the industry organizations or professional institutions. According to the experience and knowledge of experts, the weight factor corresponding to the size of the scanned object, the weight factor corresponding to the flatness, the weight factor corresponding to the material proportion, and the weight factor corresponding to the distance with other decoration materials are set.

[0052] Again, it needs to be explained that the standard size, the standard flatness, the corresponding standard proportion of the material, and the corresponding standard distance with other decoration materials are respectively the size threshold, the flatness threshold, the corresponding proportion threshold of the material, and the distance threshold corresponding to other decoration materials, for example: the standard size is 24, the standard flatness is 2, the corresponding standard proportion of the material is 1.5, and the corresponding standard distance with other decoration materials is 15.

[0053] In another specific embodiment, the judgment of whether each scanned object in each floor needs to be prewarned and fed back is as follows: C1, the robot dog adopts YOLOv8 to cut the type of each scanned object in each floor, and compares the type of each scanned object in each floor with the type of the scanned object corresponding to each standard decoration quality evaluation coefficient in the database, and then obtains the standard decoration quality evaluation coefficient corresponding to each scanned object in each floor.

[0054] C2, compare the decoration quality evaluation coefficient corresponding to each scanned object in each floor with the corresponding standard decoration quality evaluation coefficient in the database. If the decoration quality evaluation coefficient corresponding to a scanned object in a floor is less than the corresponding standard decoration quality evaluation coefficient in the database, it is determined that the scanned object in the floor needs to be prewarned and fed back. Otherwise, if the decoration quality evaluation coefficient corresponding to a scanned object in a floor is greater than or equal to the corresponding standard decoration quality evaluation coefficient in the database, it is determined that the scanned object in the floor does not need to be prewarned and fed back. In this way, whether each scanned object in each floor needs to be prewarned and fed back is judged.

[0055] In the embodiment of the application, the autonomous inspection of the robot dog significantly reduces the time and labor cost of manual inspection, improves the overall acceptance efficiency, and the system can collect and analyze data in real time, quickly generate detection results, avoid delays in traditional methods, use AI technology and image recognition, and the system eliminates the interference of human factors to ensure the objectivity and consistency of the acceptance results. Through high-precision sensors and algorithms, the decoration quality is accurately identified and evaluated, and the risk of missed detection and misjudgment is reduced.

[0056] In a specific embodiment, the quality pre-warning feedback processing of the scanned object in the floor is specifically as follows: when the decoration quality evaluation coefficient of a scanned object in a floor is lower than the standard decoration quality evaluation coefficient, a pre-warning mechanism is triggered, at this time, the robot dog marks the object as "pre-warning feedback required", and the robot dog sends the pre-warning feedback information to the designated mobile device through the wireless network, and the notification content includes: object type, floor where the object is located, decoration quality evaluation coefficient and pre-warning state required to be processed.

[0057] In the embodiment of the present application, the decoration quality acceptance module can judge and feedback quality problems in real time, quickly notify the construction team to rectify, improve the response speed, and the system compares the standard with the actual data, intelligently judges whether to issue a pre-warning, reduces the errors of manual judgment, comprehensively evaluates the quality of each area of the building, covers multiple dimensions such as size, flatness and material ratio, ensures the comprehensiveness of quality evaluation, establishes a systematic quality management process by setting standards and evaluation coefficients, and helps to improve the overall engineering quality.

[0058] The overall acceptance evaluation module is used to evaluate whether the overall construction quality of each floor corresponding to the target acceptance building meets the standard, and to evaluate and feedback the acceptance results of each floor corresponding to the target acceptance building.

[0059] In a specific embodiment, the evaluation of whether the overall construction quality of each floor corresponding to the target acceptance building meets the standard is specifically as follows: D1, after the robot dog completes the acceptance in the target acceptance building, the decoration quality evaluation coefficient corresponding to each scanned object in each type of scanned object in each floor of the target acceptance building is counted, and the decoration quality evaluation coefficient corresponding to each scanned object in each type of scanned object in each floor of the target acceptance building is accumulated, and then the decoration quality evaluation coefficient cumulative value corresponding to each type of scanned object in each floor of the target acceptance building is obtained.

[0060] D2, compare the decoration quality evaluation coefficient cumulative value corresponding to each type of scanned object in each floor of the target acceptance building with the decoration quality evaluation coefficient cumulative value corresponding to the corresponding type of scanned object set, if the decoration quality evaluation coefficient cumulative value corresponding to a type of scanned object in a floor of the target acceptance building is less than the decoration quality evaluation coefficient cumulative value corresponding to the standard corresponding type of scanned object, it is evaluated that the overall construction quality corresponding to the floor of the target acceptance building does not meet the standard, and if the decoration quality evaluation coefficient cumulative value corresponding to a type of scanned object in a floor of the target acceptance building is greater than or equal to the decoration quality evaluation coefficient cumulative value corresponding to the standard corresponding type of scanned object, it is evaluated that the overall construction quality corresponding to the floor of the target acceptance building meets the standard.

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

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

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

[0064] In the embodiment of the application, through the automatic process, the acceptance personnel can concentrate more energy on important decisions and management, improve work efficiency, the application of the system meets the development direction of buildings and smart cities, promotes the development of the building industry to a more efficient and intelligent direction, and the use of an advanced acceptance system helps to improve the competitiveness of construction units in the market and meet the needs of customers for high-quality decoration.

[0065] The above content is only an example and description of the concept of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the present application or exceed the scope defined in the specification, which should belong to the protection scope of the present application.

Claims

1. A renovation 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 for the robot dog after the preparation is completed. Decoration quality inspection module: When the robot dog is inspecting each floor of the target building, it can determine whether each scanned object on each floor needs to be given a warning. If a scanned object on a certain floor needs to be given a warning, then the quality warning feedback process will be performed on that scanned object on that floor. 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 of each scanned object on each floor. The basic information includes size, flatness, material ratio, and distance to other decoration materials. The data analysis unit is used to analyze and obtain the decoration quality assessment coefficient corresponding to each scanned object on each floor based on the basic information corresponding to each scanned object on each floor. The analysis yielded the decoration quality assessment coefficients for each scanned object on each floor. The specific analysis process is as follows: The size, flatness, ratio of each material, and distance from other decoration materials of each scanned object in each floor are respectively recorded as 、 、 and , wherein, represents the number of each floor, , u is an arbitrary integer greater than 2, represents the number of each scanned object, , n is an arbitrary integer greater than 2, represents the number of each material, , m is an arbitrary integer greater than 2, represents the number of each other decoration material, , v is an arbitrary integer greater than 2, and substituting into the calculation formula , the decoration quality evaluation coefficient of each scanned object in each floor is obtained , wherein, 、 、 、 are the standard size, standard flatness, standard ratio of each material, and standard distance from other decoration materials of the scanned object respectively, 、 、 、 are the weight factor of the scanned object size, the weight factor of the flatness, the weight factor of the material ratio, and the weight factor of the distance from other decoration materials respectively. The specific process for determining whether each scanned object on each floor requires an early warning feedback is as follows: C1. The robot dog uses YOLOv8 to cut and 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, thereby obtaining 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 less than the corresponding standard decoration quality assessment coefficient in the database, it is determined that the scanned object on that floor needs to provide 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 the scanned object on that floor does not need to provide early warning feedback. In this way, it is determined whether each scanned object on each floor needs to provide early warning feedback. Overall Acceptance Assessment Module: Used to assess whether the overall construction quality of each floor within the target building meets the standards, and to provide feedback on the assessment results for each floor within the target building.

2. The renovation project inspection system based on a bionic robot dog as described in claim 1, characterized in that, The preparation process for the robot dog is as follows: A1. Test preparation: Adjust the robot dog to a crouching position and place it in the target building area. The ground in the area should be flat, and there should be no obstacles within 1 meter around the robot dog. Check if the robot dog's battery indicator light is on and confirm that it has sufficient power. Ensure that the battery is correctly installed in the battery slot and is not loose or has poor contact. If the power is insufficient, charge or replace the battery in time. After confirming that all the above checks are correct, gently press the robot dog's power switch to turn on the robot dog's power system. A2. Exercise preparation: Connect the robot dog's WiFi network using a mobile device, ensuring a stable connection. Open the software interface and access the robot dog's control system. Remotely access the robot dog's sensing host through the connected device. In the user interface, select the exercise mode and commands, and set the robot dog's action parameters. After confirming that the set exercise commands are correct, click the send button. The commands will be transmitted to the robot dog in real time. Subsequently, the robot dog will start moving according to the received commands and adjust its direction and speed as needed. A3. Obstacle Avoidance Preparation: The built-in ultrasonic radar of the robot dog starts working, scanning the environment in front in real time to determine whether there are obstacles. The robot dog will periodically send and receive ultrasonic signals to measure the distance between itself and the obstacles in front. Combined with the robot dog's built-in algorithm, the detected data is analyzed. If an obstacle appears in front, the robot dog will quickly calculate the best obstacle avoidance path to ensure its safety. After confirming the existence of an obstacle in front, the robot dog will actively adjust its direction of travel and accelerate or decelerate.

3. The renovation project inspection system based on a bionic robot dog as described in claim 1, characterized in that, The process of planning a route for the robot dog after preparation 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-test to ensure that all sensors and system modules are operating normally. B2. The robot dog will activate the lidar system to perform a comprehensive environmental scan. The lidar emits laser beams by rotating at high speed 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. This map accurately reflects the geometry and spatial relationship of the surrounding buildings. The robot dog will convert the point cloud map into a grid map. The grid map divides the environment into regular grids, which facilitates path planning and positioning in subsequent algorithms. Each grid represents whether there are passable or impassable obstacles in the unit space. B4. Employing the hdl-localization positioning algorithm, the robot dog will accurately locate itself based on grid maps and sensor data. The robot dog can update its position and attitude in the environment in real time, ensuring highly accurate feedback on its own dynamic status. Based on the determined start point, end point, and environmental information, the robot dog will automatically plan the inspection path and generate an optimized inspection route.

4. The renovation project inspection system based on a bionic robot dog as described in claim 1, characterized in that, The process of providing quality warning feedback for the scanned object on the floor 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, an 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.

5. A construction project inspection system based on a bionic robot dog as described in claim 1, characterized in that, The assessment objective is to determine whether the overall construction quality of each floor within the building meets the standards. The specific analysis process is as follows: D1. After the robot dog completes the inspection in the target building, it calculates the decoration quality assessment coefficients of each type of scanned object in each floor of the target building and sums them up to obtain the sum of the decoration quality assessment coefficients of each type of scanned object in each floor of the target building. D2. Compare the cumulative value of the decoration quality assessment coefficients for each type of scanned object on each floor of the target building with the set cumulative value of the decoration quality assessment coefficients for the corresponding type of scanned object. If the cumulative value of the decoration quality assessment coefficients for a certain type of scanned object on a certain floor of the target building is less than the set cumulative value of the decoration quality assessment coefficients for the corresponding type of scanned object, then the overall construction quality of that floor in the target building is deemed substandard. If the cumulative value of the decoration quality assessment coefficients for a certain type of scanned object on a certain floor of the target building is greater than or equal to the set cumulative value of the decoration quality assessment coefficients for the corresponding type of scanned object, then the overall construction quality of that floor in the target building is deemed satisfactory.

6. A construction project inspection system based on a bionic robot dog as described in claim 5, characterized in that, The evaluation and feedback process for the acceptance results of each floor within the target building is as follows: F1. If the overall construction quality of a certain floor in the building being inspected fails to meet the standards, the robot dog will mark that floor as a "floor requiring rectification" and send the warning feedback information to the designated mobile device via wireless network. F2. If the overall construction quality of a certain floor in the building meets the standards, the robot dog will mark that floor as a "qualified floor" and send the warning feedback information to the designated mobile device via wireless network.

7. A construction project inspection system based on a bionic robot dog as described in claim 1, characterized in that, It also includes a database for storing the types of scanned objects corresponding to each standard decoration quality assessment coefficient.

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