Floor thickness measuring method

Abstract

The invention discloses a floor thickness measuring method. The method comprises the steps that S00, a floor thickness frame diagram of an overall building structure is established according to existing floor thickness data in the initial stage of construction; s10, scanning the flatness of the ground by using a robot dog carrying a horizontal scanner, sorting out too thick or too thin point locations, and taking the too thick or too thin point locations as optimal measurement point locations; s20, an ultrasonic floor thickness gauge is manually used for conducting floor thickness measurement on the optimal measurement point positions, and finally accurate floor thickness data are obtained; the robot dog can quickly cover a large-area measurement area, so that the time required by manual measurement is shortened, and the safety risk of manual operation is reduced; random detection is replaced by robot dog general detection, the optimal detection point position is selected, and the floor thickness measurement efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building measurement, and particularly to a method for measuring the thickness of a floor slab. Background Art

[0002] The floor slab is an important load-bearing structure of a building, and its thickness is directly related to the bearing capacity and stability of the building. By detecting the thickness of the floor slab, it can be ensured that it meets the design requirements, thus guaranteeing the overall safety of the building. Currently, in the existing technologies, there are mainly three methods for detecting the thickness of the floor slab: Method 1 is the core drilling sampling method; Method 2 is the ultrasonic detection method; Method 3 is the radar detection method; The existing three methods have the following disadvantages:

[0003] (1) It has a great dependence on manual labor and high requirements for operators, and Method 1 is also highly destructive during operation, so the measurement efficiency is low and it is not suitable for large-scale detection;

[0004] (2) Limited by the detection device, the above methods all require the use of different thickness measurement devices. Method 2 and Method 3 also require pre-installation of the equipment and have poor mobility, making it difficult to adapt to complex construction site environments;

[0005] (3) The above three methods generally adopt the random method to select detection points. Although this method is simple and easy to operate, the uncertainty is too high, and there is a high possibility that the selected detection points are not the optimal solutions, unable to ensure that the results can reflect the standard of the quality of the floor slab, and unable to adjust the measurement strategy in a timely manner according to needs. Summary of the Invention

[0006] In order to overcome the defects of the existing technology, the technical problem to be solved by the present invention is to propose a method for measuring the thickness of a floor slab, which can efficiently and quickly select the optimal detection points by improving the degree of automation, reduce manual operation, and improve the accuracy and reliability of the measurement results.

[0007] To achieve this purpose, the present invention adopts the following technical solutions:

[0008] A method for measuring the thickness of a floor slab provided by the present invention, S00: Establish a floor slab thickness framework diagram of the overall building structure based on the existing floor slab thickness data in the preliminary construction stage; S10: Use a robotic dog equipped with a horizontal scanner to travel along the optimal route to scan the ground flatness, sort out the points that are too thick or too thin, and use them as the best measurement points; S20: Manually use an ultrasonic floor slab thickness gauge to measure the thickness of these best measurement points, and finally obtain accurate floor slab thickness data.

[0009] In step S10, the steps to obtain the best measurement points are as follows: S11: Divide the floor slab into multiple grid units, and the grid size is dynamically adjusted according to the length-width ratio of the floor slab. According to the structural characteristics of the floor slab (such as column positions, prestressed areas, etc.), divide the floor slab into key areas and general areas; S12: Use a diamond-shaped route to scan in the general area. In the key area, the machine dog travels along the optimal route. The objective function expression of the optimal route for the machine dog to travel is:

[0010] f(x,y) = ω 1 ·L(x,y) + ω 2 ·C(x,y) and ω 1 + ω 2 = 1

[0011] where (x,y) is the coordinate of the machine dog on the floor slab during scanning, representing the specific position of the machine dog on the floor slab during scanning, f(x,y) is the evaluation function of the travel route; L(x,y) is the path length from the starting point to the point (x,y); C(x,y) is the number of the best measurement points covered from the starting point to the point (x,y); ω 1 is the weight coefficient of the path length L, used to measure the importance of the path length in path planning; ω 2 is the weight coefficient of the number of the best measurement points C covered, used to measure the importance of the number of the best measurement points covered in path planning.

[0012] The beneficial effects of the present invention are as follows:

[0013] (1) The machine dog can quickly cover a large measurement area, reduce the time required for manual measurement, and reduce the safety risk of manual operation; by using the general measurement of the machine dog instead of random detection and selecting the best detection points, the measurement efficiency of the floor slab thickness is improved;

[0014] (2) The combination of automated equipment and manual measurement improves the accuracy and reliability of the measurement results; the application of automated equipment reduces the errors caused by human operation and improves the objectivity and consistency of the data. Description of the Drawings

[0015] Figure 1 is a schematic flow chart of a method for measuring the thickness of a floor slab provided in a specific embodiment of the present invention; Specific Embodiments

[0016] The technical solution of the present invention will be further described below with reference to the drawings and through specific embodiments.

[0017] Example 1: As Figure 1 shown, the present invention provides a method for measuring the thickness of a floor slab, including the following steps:

[0018] S00: Establish a floor thickness framework diagram of the overall building structure based on the existing floor thickness data in the preliminary construction stage; thus providing basic structural information for the travel route of the robotic dog, enabling it to avoid some obvious obstacles or structurally weak areas;

[0019] S10: Use the robotic dog equipped with a horizontal scanner to travel along the optimal route to scan the ground flatness, sort out the points that are too thick or too thin, and use them as the best measurement points; during the scanning process, the entire measurement area needs to be covered to ensure that the points that are too thick or too thin can be comprehensively detected. The travel route needs to ensure the uniformity and comprehensiveness of the scanning. When sorting out the points that are too thick or too thin as the best measurement points, the travel route of the robotic dog needs to be able to reach these points efficiently, reducing unnecessary repeated paths and time waste to ensure the measurement efficiency, reducing blind manual sampling, and improving the measurement efficiency and representativeness;

[0020] The robotic dog can also be equipped with a lidar, an angle sensor, and a vision camera to fully sense the floor information for the flatness analysis of the floor and the travel of the robotic dog.

[0021] S20: Manually use an ultrasonic floor thickness gauge to measure the floor thickness at these best measurement points, and finally obtain accurate floor thickness data; thus combining automated preliminary screening and manual precise measurement, taking into account both efficiency and accuracy, and forming a closed-loop optimization process;

[0022] In step S10, the steps to obtain the best measurement points are as follows:

[0023] S11: Divide the floor into multiple grid units, and the grid size is dynamically adjusted according to the length-width ratio of the floor. According to the structural characteristics of the floor (such as column positions, prestressed areas, etc.), divide the floor into key areas and general areas;

[0024] In step S11, when the length-width ratio of the floor is greater than 2, the grid density in the long side direction is greater than the grid density in the short side direction to improve the scanning accuracy;

[0025] S12: In the general area, use a diamond-shaped route for scanning to efficiently cover a large area while reducing path repetition. Starting from the center of the area, expand outward according to the diamond pattern, and the path spacing is dynamically adjusted according to the grid size; in the key area, consider the path length on the optimal travel route of the robotic dog and the route also needs to cover all the best measurement points. Thus, establish a coordinate system on the floor to be detected and generate the objective function expression of the travel route:

[0026] f(x,y) = ω 1 ·L(x,y) + ω 2 ·C(x,y) and ω 1 + ω 2= 1

[0027] Among them, (x, y) is the coordinate on the floor slab during the scanning of the robotic dog, representing the specific position of the robotic dog on the floor slab during scanning, and f(x, y) is the evaluation function of the travel route; L(x, y) is the path length from the starting point to the point (x, y); C(x, y) is the number of the best measurement points covered from the starting point to the point (x, y); ω 1 is the weight coefficient of the path length L, used to measure the importance of the path length in path planning; ω 2 is the weight coefficient of the number C of the best measurement points covered, used to measure the importance of the number of the best measurement points covered in path planning.

[0028] In this way, through the above functions, during the scanning of the robotic dog, data can be fed back in real time and the travel route can be dynamically adjusted to reduce sharp turns and unnecessary path fluctuations; when it is found that the flatness of some areas is abnormal, the robotic dog can temporarily adjust the path correction to increase the scanning density.

[0029] When performing path planning under different situations and different requirements, ω 1 and ω 2 in the above expression will have different emphases. If a shorter path is desired, the value of ω 1 can be increased, and at this time it is more inclined to the path length L; if more of the best measurement points are desired to be covered, the value of ω 2 can be increased, and at this time it is more inclined to cover more of the best measurement points; further, in order to obtain the optimal travel route in actual use under different situations, ω 1 and ω 2 can be adjusted according to the following method:

[0030] (1) Based on the complexity of dividing the floor slab into grids: Let the total number of grids after dividing the floor slab into grids be A, and the number of grids with obstacles after dividing the floor slab into grids be a. In this way, the complexity of dividing the floor slab into grids can be obtained as:

[0031]

[0032] Furthermore, the values of ω 1 and ω 2 can be obtained:

[0033] When σ is relatively large, ω 1 is relatively small, and ω 2 is relatively large. At this time, it is more inclined to cover more of the best measurement points;

[0034] When σ is relatively small, ω 1 is relatively large, and ω 2 is relatively small. At this time, it is more inclined to the path length L being shorter;

[0035] (2) Time limit based on path planning: Let the path planning time limit be T, and the current time used be t. Then:

[0036] When t is close to T, increase the value of ω 1 and decrease the value of ω 2 . At this time, it is more inclined to a shorter path length L to quickly find a feasible path and complete the measurement within the limited time;

[0037] When t is much less than T, decrease the value of ω 1 and increase the value of ω 2 . At this time, it is more inclined to cover more optimal measurement points to increase the weight of the optimal measurement points and optimize the path coverage rate;

[0038] In summary, in the case of tight time constraints, increase the weight of the path length to quickly find a feasible path and improve the measurement efficiency; in the case of loose time constraints, increase the weight of covering the key point count to optimize the path coverage rate and improve the measurement accuracy.

[0039] In practical applications, the above function model can dynamically adjust the path according to the length-width ratio and structural characteristics of the floor slab, and is applicable to floor slabs of different sizes and shapes; combining the use of diamond-shaped routes in general areas and f(x,y) function model paths in key areas can efficiently cover large areas while ensuring the measurement accuracy of key areas. The adaptive adjustment mechanism enables the model to respond to environmental changes in real time, improving the reliability and accuracy of the measurement, that is, combining the automated preliminary screening of the robot dog with manual review to form a closed-loop process of "rough measurement → intelligent screening → precise measurement", taking into account both efficiency and accuracy, and solving the problems of insufficient accuracy of fully automated equipment or low manual efficiency.

[0040] Embodiment 2: In step S10, the robotic dog includes a main robotic dog and several sub-robotic dogs. The main robotic dog performs dynamic partitioning based on the floor slab framework diagram and assigns task priorities to the sub-robotic dogs. That is, the main robotic dog divides the floor slab into multiple sub-regions according to the floor slab thickness data, structural features (such as load-bearing walls, prestressed areas), and real-time scanning feedback, dynamically marks key areas, and then dynamically assigns tasks according to the status of the sub-robotic dogs (battery level, computing load, scanning progress) to avoid overloading the sub-robotic dogs. Task assignment is also carried out in combination with the regional risk level (such as large thickness deviation, complex structure) and timeliness requirements, and the scanning order is dynamically adjusted. The sub-robotic dogs dynamically adjust the scanning according to the regional importance level fed back by the host, and generate a refined path for the information transmitted by the main robotic dog, avoiding local obstacles and covering dense measurement points. During the collaborative measurement of the main robotic dog and several sub-robotic dogs, the main robotic dog dynamically corrects the global path by calculating real-time scanning data. When a sub-robotic dog discovers an unforeseen obstacle or abnormal area, it triggers local path replanning and requests global task adjustment from the host.

[0041] On this basis, to adapt to the main robotic dog and several sub-robotic dogs in the master-slave mode, the objective function expression of the travel route is further optimized:

[0042] f(x,y) = ω 1 ·L(x,y) + ω 2 ·C(x,y) + ω 3 ·G(x,y) + ω 4 ·Q(x,y) and ω 1 + ω 2 + ω 3 + ω 4 = 1

[0043] where G(x,y) is the time cost from the starting point to point (x,y); Q(x,y) is the energy consumption cost from the starting point to point (x,y); ω 3 is the weight coefficient of the time cost G, which is used to measure the importance of time (i.e., task urgency) in path planning; ω 4 is the weight coefficient of the energy consumption cost, which is used to measure the importance of the energy consumption cost (i.e., resource limitation) in path planning.

[0044] Thus, through the above function, during the scanning of the main robotic dog and several sub-robotic dogs, data can be fed back in real time and the travel route can be dynamically adjusted. At the same time, the main robotic dog predicts the path crossing risk of multiple sub-robotic dogs, inserts waiting instructions in advance or fine-tunes the path. When a sub-robotic dog loses contact, the main robotic dog automatically takes over its unfinished tasks and redistributes them to other sub-robotic dogs.

[0045] In summary, when the master robotic dog and several slave robotic dogs in the master-slave mode are performing measurements, they adopt a strategy of hierarchical planning and dynamic cooperation, achieving a balance between global path efficiency and resource constraints, and enabling the local path to flexibly adapt to complex environments. During this process, multi-robot cooperation reduces repetition and conflicts, and the measurement strategy can be flexibly adjusted to cope with various situations, significantly reducing the task completion time.

[0046] The present invention is described through preferred embodiments. Those skilled in the art will appreciate that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the present invention. The present invention is not limited by the specific embodiments disclosed herein, and other embodiments falling within the scope of the claims of this application belong to the scope of protection of the present invention.

Claims

1. A method for measuring floor thickness, characterized in that: The following steps are involved: S00: Establish the floor thickness framework diagram of the overall building structure based on the floor thickness data available in the initial construction stage; S10: Use a robot dog equipped with a horizontal scanner to scan the flatness of the ground along the optimal route, sort out the points that are too thick or too thin, and use them as the best measurement points; S20: Manually use an ultrasonic floor thickness gauge to measure the floor thickness at these optimal measurement points, and finally obtain accurate floor thickness data.

2. A floor thickness measurement method according to claim 1, characterized in that: In step S10, the robot dog is also equipped with a laser radar, an angle sensor and a visual camera.

3. A floor thickness measurement method according to claim 2, characterized in that: In step S10, the steps of obtaining the best measurement point are: S11: Divide the floor slab into multiple grid units. The grid size is dynamically adjusted according to the length-width ratio of the floor slab. The floor slab is divided into key areas and general areas according to the structural characteristics of the floor slab (such as column positions, prestressed areas, etc.); S12: A diamond-shaped route is used for scanning in the general area, and in the key area, the robot dog travels along the optimal route.

4. A floor thickness measurement method according to claim 3, characterized in that: In step S11, when the aspect ratio of the floor slab is greater than 2, the grid density in the long side direction is greater than the grid density in the wide side direction.

5. A floor thickness measurement method according to claim 4, characterized in that: In step S12, the objective function expression of the optimal route for the robot dog to travel is: f(x,y)=ω1·L(x,y)+ω2·C(x,y) and ω1+ω2=1 Among them, (x, y) is the coordinate of the robot dog on the floor when scanning, which indicates the specific position of the robot dog on the floor when scanning, f(x, y) is the evaluation function of the travel route; L(x, y) is the path length from the starting point to the point (x, y); C(x, y) is the number of optimal measurement points covered from the starting point to the point (x, y); ω1 is the weight coefficient of the path length L, which is used to measure the importance of the path length in path planning; ω2 is the weight coefficient of the optimal number of measurement points covered C, which is used to measure the importance of the optimal number of measurement points covered in path planning.

6. A floor thickness measurement method according to claim 4, characterized in that: In step S10, the robot dog includes a main robot dog and several slave robot dogs. The main robot dog performs dynamic partitioning based on the floor frame diagram and assigns task priorities to the slave robot dogs. The slave robot dogs dynamically adjust the scan according to the regional importance level fed back by the host, and generate refined paths for the information transmitted by the main robot dog to avoid local obstacles and cover dense measurement points.

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