Interior wall brick embedding precision detection method and system based on visual identification

Abstract

The invention discloses an interior wall brick embedding precision detection method and system based on visual identification, relates to the technical field of interior wall brick detection, solves the technical problem that the construction process and construction influence of interior wall bricks cannot be detected in the prior art, and particularly relates to a method and a system for detecting the embedding precision of the interior wall bricks by a wall brick surface detection unit for detecting the surfaces of the interior wall bricks. Wall brick information is collected according to the collected pictures, surface detection coefficients are obtained through calculation, and whether surface defects exist in the current to-be-installed inner wall brick or not is deduced according to coefficient comparison; the construction process detection unit performs construction detection on the construction process of the inner wall bricks, acquires slurry influence information and slurry use information, and deduces whether the construction detection of the current inner wall bricks is abnormal or not according to information comparison; the construction influence detection unit is used for carrying out construction influence detection on the real-time construction process, collecting solidification data and bonding data, and analyzing and deducing whether the construction influence detection is abnormal or not according to the data; if yes, reworking is carried out, and if not, pointing is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of interior wall tile detection, and in particular to a method and system for detecting the fitting accuracy of interior wall tiles based on visual recognition. Background Art

[0002] Interior wall tiles are ceramic tiles used for interior wall decoration. They mainly protect the wall and beautify the indoor environment. Compared with exterior wall tiles, interior wall tiles do not need to have the same high frost resistance and wear resistance as exterior wall tiles, but have higher requirements for decoration, environmental protection and safety.

[0003] However, in the prior art, it is impossible to ensure that the surface inspection of the interior wall tiles is normal when the interior wall tiles are installed on the wall, resulting in a decrease in the fitting accuracy. At the same time, it is impossible to detect the construction process and construction impact of the interior wall tiles, so that it is impossible to ensure the timeliness of the construction inspection of the interior wall tiles and the fitting accuracy of the interior wall tiles.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a method and system for detecting the fitting accuracy of interior wall tiles based on visual recognition.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An interior wall tile fitting accuracy detection system based on visual recognition includes a fitting detection platform, wherein the fitting detection platform is communicatively connected to a wall tile surface detection unit, a construction process detection unit, a construction impact detection unit, and a grouting environment detection unit; The wall tile surface detection unit performs surface detection on the interior wall tiles, divides the surface of the interior wall tiles to be installed into the inner surface and the outer surface, and collects pictures of the inner surface and the outer surface respectively according to the visual recognition technology, collects wall tile information according to the collected pictures, and obtains the surface detection coefficient by calculation, and infers whether the interior wall tiles to be installed have surface defects according to the coefficient comparison, if yes, repairs them, if not, use them as construction materials; The construction process detection unit performs construction detection on the construction process of the interior wall tiles, collects mud impact information and mud usage information, and infers whether the current construction detection of the interior wall tiles is abnormal based on information comparison. If yes, the mud is redistributed, otherwise, the construction continues; The construction impact detection unit performs construction impact detection on the real-time construction process, collects solidification data and bonding data, and infers whether the construction impact detection is abnormal based on data analysis; if yes, rework is performed; if not, grouting is performed; The grouting environment detection unit detects the grouting process of the inner wall tiles, collects grouting efficiency data and grouting detection data, and judges the execution efficiency of the grouting environment of the inner wall tiles based on data analysis. If it is inefficient, the grouting will be improved; if it is efficient, the next inner wall tile construction will be carried out.

[0007] As a preferred embodiment of the present invention, the wall tile information includes the connection distance of the corresponding surface cracks at the same position on the outer surface and the inner surface of the interior wall tiles, the cumulative increase span of the corresponding water accumulation at the deformation position of the inner surface of the interior wall tiles, and the horizontality deviation value of the corresponding transverse wall tile connection surface of the inner and outer surfaces of the adjacent connected interior wall tiles.

[0008] As a preferred embodiment of the present invention, if the surface detection coefficient of the interior wall tiles to be installed exceeds the surface detection coefficient threshold, a surface defect abnormal signal is generated; if the surface detection coefficient of the interior wall tiles to be installed exceeds the surface detection coefficient threshold, a surface detection normal signal is generated.

[0009] As a preferred embodiment of the present invention, the mud impact information and the mud usage information are respectively the environmental parameter floating span of the environment corresponding to the same mud material in the wall tile laying stage during the interior wall tile construction process, and the coverage area of ​​the mud in the tile laying area during the wall tile laying stage during the interior wall tile construction process and the coverage area of ​​the inner surface bonded by the mud.

[0010] As a preferred embodiment of the present invention, if the mud impact information exceeds the environmental parameter floating span threshold, or the mud usage information does not exceed the coverage area and the threshold, a construction rectification signal is generated; if the mud impact information does not exceed the environmental parameter floating span threshold, and the mud usage information exceeds the coverage area and the threshold, a normal construction signal is generated.

[0011] As a preferred embodiment of the present invention, the solidification data and the bonding data are respectively the gap drop speed of the interior wall tiles corresponding to the mud solidification stage in the corresponding area of ​​the completed interior wall tiles, and the ratio of the hollow area of ​​the interior wall tiles corresponding to the completed construction to the mud bonding area.

[0012] As a preferred embodiment of the present invention, if the coagulation data does not exceed the gap decrease speed threshold, or the bonding data exceeds the area ratio threshold, a high risk signal for the fitting accuracy is generated; if the coagulation data exceeds the gap decrease speed threshold and the bonding data does not exceed the area ratio threshold, a low risk signal for the fitting accuracy is generated.

[0013] As a preferred embodiment of the present invention, the grouting efficiency data and the grouting detection data are respectively the floating deviation value of the horizontality between adjacent interior wall tiles corresponding to the starting moment of the interior wall tile grouting link, and the maximum embeddable depth value of the detection tool of the grouting agent around the interior wall tiles during the execution of the interior wall tile grouting environment.

[0014] As a preferred embodiment of the present invention, if the grouting efficiency data exceeds the floating deviation threshold, or the grouting detection data exceeds the maximum embeddable depth threshold, an accuracy floating risk signal is generated; if the grouting efficiency data does not exceed the floating deviation threshold, and the grouting detection data does not exceed the maximum embeddable depth threshold, an accuracy accurate signal is generated.

[0015] The method for detecting the fitting accuracy of interior wall tiles based on visual recognition is characterized by being a system for detecting the fitting accuracy of interior wall tiles based on visual recognition as described in any one of claims 1 to 9 above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the surface of the interior wall tiles is inspected, and the surface inspection is used to infer whether the performance of the interior wall tiles before use affects the fitting accuracy. If the surface inspection of the interior wall tiles is abnormal, the performance of the interior wall tiles will affect the construction efficiency of the mud, etc., resulting in the position deviation of the interior wall tiles, which increases the risk of deviation of the fitting accuracy of the interior wall tiles, thereby reducing the fitting construction efficiency of the interior wall tiles and increasing the wear of all installed interior wall tiles. Carry out construction inspection on the construction process of interior wall tiles, and infer whether there is any fitting accuracy deviation in the current interior wall tile pasting process based on the construction process inspection, so as to ensure the timeliness of the construction inspection of interior wall tiles, avoid the existence of tiles with fitting accuracy deviation in the completed workload of interior wall tiles, delay the progress of interior wall tile construction, and at the same time avoid abnormal fitting accuracy and reduce the risk of damage when replacing interior wall tiles.

[0017] 2. In the present invention, construction impact detection is performed on the real-time construction process. Through the construction impact detection, it is inferred whether there is any construction abnormality in the interior wall tiles that have been completed, so as to infer whether there is any deviation in the fitting accuracy of the interior wall tiles. The interior wall tiles that have been completed are detected through real-time construction impact analysis to avoid the situation where the detection accuracy is qualified at the time of construction but the accuracy deviation occurs after a period of time. The efficiency and timeliness of the fitting of interior wall tiles are improved through impact detection; The grouting process of the interior wall tiles is detected to infer whether there are any abnormalities in the execution of the grouting process during the construction of the interior wall tiles. Through the grouting process detection, it is inferred whether there are any later impacts on the current interior wall tile construction, so as to ensure the real-time construction efficiency of the interior wall tiles. When the grouting process detection is normal, it indicates that the current interior wall tile construction efficiency meets the actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 It is a principle block diagram of the system of the present invention; Figure 2 It is a principle block diagram of the method of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0021] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] See also Figure 1 As shown, the interior wall tile fitting accuracy detection system based on visual recognition includes a fitting detection platform, wherein the fitting detection platform is communicatively connected with a wall tile surface detection unit, a construction process detection unit, a construction impact detection unit, and a grouting environment detection unit; The fitting detection platform generates a wall tile surface detection signal and sends the wall tile surface detection signal to the wall tile surface detection unit. After receiving the wall tile surface detection signal, the wall tile surface detection unit performs surface detection on the inner wall tiles. Through surface detection, it is inferred whether the performance of the inner wall tiles before use affects the fitting accuracy. If the surface detection of the inner wall tiles is abnormal, the performance of the inner wall tiles will affect the efficiency of the construction of mud, etc., resulting in positional displacement of the inner wall tiles, increasing the risk of displacement of the fitting accuracy of the inner wall tiles, thereby reducing the construction efficiency of the inner wall tiles and increasing the wear of all installed inner wall tiles. The surface of the interior wall tiles to be installed is divided into an inner surface and an outer surface, and pictures of the inner surface and the outer surface are collected respectively according to the visual recognition technology, and the connection distance of the corresponding surface cracks at the same position of the outer surface and the inner surface of the interior wall tiles is obtained according to the collected pictures, and the cumulative increase span of the corresponding water accumulation at the deformation position of the inner surface of the interior wall tiles is obtained, and the connection distance of the corresponding surface cracks at the same position of the outer surface and the inner surface of the interior wall tiles and the cumulative increase span of the corresponding water accumulation at the deformation position of the inner surface of the interior wall tiles are marked as LT and ZK respectively, and the connection distance is expressed as the crack distance of the corresponding inner and outer cracks penetrating and connecting; Obtain the horizontality deviation value of the corresponding transverse wall tile connection surface of the inner and outer surfaces of the adjacent connected interior wall tiles, and mark the horizontality deviation value of the corresponding transverse wall tile connection surface of the inner and outer surfaces of the adjacent connected interior wall tiles as SP; The above collected information is uniformly marked as wall tile information, and substituted into the formula to obtain the surface detection coefficient of the interior wall tiles to be installed, where the formula is: , where G is the surface detection coefficient, hyt1, hyt2 and hyt3 are preset proportional coefficients, and β is the error correction factor with a value of 0.97; Compare the surface detection factor of the interior wall tiles to be installed with the surface detection factor threshold: If the surface detection coefficient of the interior wall tile to be installed exceeds the surface detection coefficient threshold, it is inferred that the interior wall tile to be installed currently has surface defects, a surface defect abnormality signal is generated and the surface defect abnormality signal is sent to the embedded detection platform. After the embedded detection platform receives the surface defect abnormality signal, the current interior wall tile is repaired and is not set as a construction material temporarily. If the surface detection coefficient of the interior wall tile to be installed exceeds the surface detection coefficient threshold, it is inferred that the interior wall tile to be installed currently has no surface defects, a surface detection normal signal is generated and sent to the embedded detection platform, and after the embedded detection platform receives the surface detection normal signal, the current interior wall tile is set as a construction material; At the same time, a construction process detection signal is generated and sent to a construction process detection unit. After receiving the construction process detection signal, the construction process of the interior wall tiles is detected. According to the construction process detection, it is inferred whether there is a fitting accuracy deviation in the current interior wall tile pasting process, so as to ensure the timeliness of the construction detection of the interior wall tiles, avoid the existence of wall tiles with fitting accuracy deviation in the completed workload of the interior wall tiles, delay the progress of the interior wall tile construction, and avoid fitting accuracy abnormalities, reducing the risk of damage to the interior wall tiles when they are replaced; The environmental parameter floating span of the environment corresponding to the same mud material in the wall tile installation stage during the interior wall tile construction process is obtained, where the environmental parameters are expressed as temperature and humidity; at the same time, the sum of the coverage area of ​​the mud in the tile-laying area in the wall tile installation stage during the interior wall tile construction process and the coverage area of ​​the mud attached to the inner surface is obtained, and the environmental parameter floating span of the environment corresponding to the same mud material in the wall tile installation stage during the interior wall tile construction process and the sum of the coverage area of ​​the mud in the tile-laying area in the wall tile installation stage during the interior wall tile construction process and the coverage area of ​​the mud attached to the inner surface are marked as mud impact information and mud usage information, respectively, and compared with the environmental parameter floating span threshold and the coverage area and threshold respectively: If the environmental parameter floating span of the environment corresponding to the same mud material in the wall tile installation stage during the interior wall tile construction process exceeds the environmental parameter floating span threshold, or the sum of the corresponding coverage areas of the mud in the tile installation area and the coverage area of ​​the inner surface of the mud installation during the wall tile construction process does not exceed the coverage area and the threshold, it is inferred that the current interior wall tile construction detection is abnormal, and a construction rectification signal is generated and sent to the chimeric detection platform. After receiving the construction rectification signal, the chimeric detection platform reallocates the mud consumed in the current construction and adjusts the mud use efficiency; If the environmental parameter floating span of the environment corresponding to the same mud material in the wall tile installation stage during the interior wall tile construction process does not exceed the environmental parameter floating span threshold, and the sum of the corresponding coverage areas of the mud in the tile installation area during the wall tile installation stage during the interior wall tile construction process and the coverage area of ​​the mud-attached inner surface exceeds the coverage area and threshold, it is inferred that the current interior wall tile construction detection is normal, and a normal construction signal is generated and sent to the embedded detection platform; At the same time, a construction impact detection signal is generated and sent to the construction impact detection unit. After receiving the construction impact detection signal, the construction impact detection unit performs construction impact detection on the real-time construction process. Through the construction impact detection, it is inferred whether there is any construction abnormality in the interior wall tiles that have been completed, and thus it is inferred whether there is any deviation in the fitting accuracy of the interior wall tiles. Through real-time construction impact analysis, the interior wall tiles that have been completed are detected to avoid the situation where the detection accuracy is qualified at the moment of construction but the accuracy deviation occurs after a period of time. Through the impact detection, the efficiency and timeliness of the fitting of the interior wall tiles are improved; The gap decrease speed of the corresponding inner wall tiles in the mud solidification stage in the corresponding area of ​​the completed inner wall tiles is obtained. If the mud water content is too high, the mud solidification speed will decrease, and the bonding performance of the inner wall tiles will decrease, which is prone to fitting deviation. At the same time, the ratio of the hollow area to the mud bonding area of ​​the corresponding area of ​​the completed inner wall tiles is obtained, and the gap decrease speed of the corresponding inner wall tiles in the mud solidification stage in the corresponding area of ​​the completed inner wall tiles and the ratio of the hollow area to the mud bonding area of ​​the completed inner wall tiles are marked as solidification data and bonding data, respectively, and compared with the gap decrease speed threshold and area ratio threshold, respectively: If the gap decrease speed of the corresponding inner wall tiles in the mud solidification stage in the corresponding area of ​​the completed inner wall tiles does not exceed the gap decrease speed threshold, or the ratio of the hollow area to the mud bonding area in the corresponding area of ​​the completed inner wall tiles exceeds the area ratio threshold, it is inferred that the construction affects the detection abnormality, and a high-risk signal of mosaic accuracy is generated and sent to the mosaic detection platform. After receiving the high-risk signal of mosaic accuracy, the mosaic detection platform reworks the current inner wall tiles and readjusts the mud materials used; If the gap drop speed of the corresponding inner wall tiles in the mud solidification stage in the area corresponding to the inner wall tiles that have completed construction exceeds the gap drop speed threshold, and the ratio of the hollow area to the mud bonding area in the area corresponding to the inner wall tiles that have completed construction does not exceed the area ratio threshold, it is inferred that the construction impact detection is normal, and a low-risk signal for chimeric accuracy is generated and sent to the chimeric detection platform; After receiving the low-risk signal of fitting accuracy, the fitting detection platform generates a grouting environment detection signal and sends the grouting environment detection signal to the grouting environment detection unit. After receiving the grouting environment detection signal, the grouting environment detection unit detects the grouting link of the inner wall tiles, and infers whether there is any abnormality in the execution of the grouting link during the construction of the inner wall tiles. Through the grouting link detection, it is inferred whether there is any later impact on the current inner wall tile construction, so as to ensure the real-time construction efficiency of the inner wall tiles. When the grouting link detection is normal, it indicates that the current construction efficiency of the inner wall tiles meets the actual needs. The floating deviation value of the horizontality between adjacent inner wall tiles corresponding to the starting time of the inner wall tile grouting link is obtained, and the maximum embeddable depth value of the detection tool of the grouting agent around the inner wall tiles during the execution of the inner wall tile grouting environment is obtained, and the floating deviation value of the horizontality between adjacent inner wall tiles corresponding to the starting time of the inner wall tile grouting link and the maximum embeddable depth value of the detection tool of the grouting agent around the inner wall tiles during the execution of the inner wall tile grouting environment are marked as grouting efficiency data and grouting detection data, respectively, and compared with the floating deviation threshold and the maximum embeddable depth threshold respectively: the detection tool is represented by a toothpick in the prior art or a metal material used for wall tile construction; If the floating deviation value of the horizontality between adjacent inner wall tiles corresponding to the start time of the inner wall tile grouting link execution exceeds the floating deviation threshold, or the maximum embeddable depth value of the detection tool of the grouting agent around the inner wall tiles during the execution of the inner wall tile grouting environment exceeds the maximum embeddable depth threshold, it is inferred that the execution of the inner wall tile grouting environment is inefficient, and an accuracy floating risk signal is generated and sent to the mosaic detection platform. After receiving the accuracy floating risk signal, the mosaic detection platform adjusts the mosaic accuracy of the inner wall tiles in the corresponding area and performs grouting improvement; If the floating deviation value of the levelness between adjacent inner wall tiles corresponding to the start time of the inner wall tile grouting link execution does not exceed the floating deviation threshold, and the maximum embeddable depth value of the detection tool of the grouting agent around the inner wall tiles during the execution of the inner wall tile grouting environment does not exceed the maximum embeddable depth threshold, it is inferred that the inner wall tile grouting environment execution is efficient, and an accuracy signal is generated and the accuracy risk signal is sent to the embedded detection platform; See also Figure 2 As shown in the figure, the internal wall tile mosaic accuracy detection method based on visual recognition, the specific accuracy detection method process is as follows: Surface inspection of wall tiles: Surface inspection of interior wall tiles. The surface of the interior wall tiles to be installed is divided into the inner surface and the outer surface. Images of the inner surface and the outer surface are collected respectively based on visual recognition technology. The wall tile information is collected based on the collected images, and the surface inspection coefficient is obtained by calculation. Based on the coefficient comparison, it is inferred whether there are surface defects in the interior wall tiles to be installed. If yes, repair them. If not, use them as construction materials. Construction process detection: Conduct construction detection on the construction process of interior wall tiles, collect mud impact information and mud use information, and infer whether the current construction detection of interior wall tiles is abnormal based on information comparison. If yes, the mud will be redistributed, otherwise, the construction will continue; Construction impact detection: Conduct construction impact detection on the real-time construction process, collect solidification data and bonding data, and infer whether the construction impact detection is abnormal based on data analysis; if yes, rework is performed; if not, point joints are performed; The grouting environment is tested, and the grouting process of the internal wall tiles is tested. The grouting efficiency data and grouting detection data are collected. The efficiency of the grouting environment of the internal wall tiles is judged based on the data analysis. If it is inefficient, the grouting will be improved. If it is efficient, the next internal wall tile construction will be carried out.

[0023] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions; The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The interior wall tile fitting accuracy detection system based on visual recognition is characterized by: It includes a fitting detection platform, wherein the fitting detection platform is communicatively connected with a wall tile surface detection unit, a construction process detection unit, a construction impact detection unit, and a grouting environment detection unit; The wall tile surface detection unit performs surface detection on the interior wall tiles, divides the surface of the interior wall tiles to be installed into the inner surface and the outer surface, and collects pictures of the inner surface and the outer surface respectively according to the visual recognition technology, collects wall tile information according to the collected pictures, and obtains the surface detection coefficient by calculation, and infers whether the interior wall tiles to be installed have surface defects according to the coefficient comparison, if yes, repairs them, if not, use them as construction materials; The construction process detection unit performs construction detection on the construction process of the interior wall tiles, collects mud impact information and mud usage information, and infers whether the current construction detection of the interior wall tiles is abnormal based on information comparison. If yes, the mud is redistributed, otherwise, the construction continues; The construction impact detection unit performs construction impact detection on the real-time construction process, collects solidification data and bonding data, and infers whether the construction impact detection is abnormal based on data analysis; if yes, rework is performed; if not, grouting is performed; The grouting environment detection unit detects the grouting process of the inner wall tiles, collects grouting efficiency data and grouting detection data, and judges the execution efficiency of the grouting environment of the inner wall tiles based on data analysis. If it is inefficient, the grouting will be improved; if it is efficient, the next inner wall tile construction will be carried out.

2. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 1 is characterized in that: The wall tile information includes the connection distance of the corresponding surface cracks at the same position on the outer and inner surfaces of the interior wall tiles, the cumulative increase span of the corresponding water accumulation at the deformation position of the inner surface of the interior wall tiles, and the horizontality deviation value of the corresponding transverse wall tile connection surface of the inner and outer surfaces of the adjacent connected interior wall tiles.

3. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 2 is characterized in that: If the surface detection coefficient of the interior wall tiles to be installed exceeds the surface detection coefficient threshold, a surface defect abnormal signal is generated; if the surface detection coefficient of the interior wall tiles to be installed exceeds the surface detection coefficient threshold, a surface detection normal signal is generated.

4. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 1 is characterized in that: The mud impact information and mud usage information are respectively the environmental parameter floating span of the environment corresponding to the same mud material in the wall tile installation stage during the interior wall tile construction process, and the sum of the coverage area of ​​the mud in the tile installation area during the wall tile installation stage during the interior wall tile construction process and the coverage area of ​​the inner surface fitted with the mud.

5. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 4 is characterized in that: If the mud impact information exceeds the environmental parameter floating span threshold, or the mud usage information does not exceed the coverage area and threshold, a construction rectification signal is generated; if the mud impact information does not exceed the environmental parameter floating span threshold, and the mud usage information exceeds the coverage area and threshold, a normal construction signal is generated.

6. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 1 is characterized in that: The solidification data and bonding data are respectively the gap reduction speed of the corresponding inner wall tiles in the mud solidification stage in the corresponding area of ​​the completed inner wall tiles, and the ratio of the hollow area of ​​the corresponding area of ​​the completed inner wall tiles to the mud bonding area.

7. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 6 is characterized in that: If the coagulation data does not exceed the gap drop speed threshold, or the bonding data exceeds the area ratio threshold, a high-risk signal for fitting accuracy is generated; if the coagulation data exceeds the gap drop speed threshold and the bonding data does not exceed the area ratio threshold, a low-risk signal for fitting accuracy is generated.

8. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 1 is characterized in that: The grouting efficiency data and grouting detection data are respectively the floating deviation value of the horizontality between adjacent interior wall tiles corresponding to the starting moment of the interior wall tile grouting process and the maximum embeddable depth value of the detection tool of the grouting agent around the interior wall tiles during the execution of the interior wall tile grouting environment.

9. The interior wall tile fitting accuracy detection system based on visual recognition according to claim 8 is characterized in that: If the grouting efficiency data exceeds the floating deviation threshold, or the grouting detection data exceeds the maximum embeddable depth threshold, a floating accuracy risk signal is generated; if the grouting efficiency data does not exceed the floating deviation threshold, and the grouting detection data does not exceed the maximum embeddable depth threshold, a precise accuracy signal is generated.

10. A method for detecting the fitting accuracy of interior wall tiles based on visual recognition, characterized in that: An interior wall tile fitting accuracy detection system based on visual recognition as described in any one of claims 1 to 9 above.

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