Floor tile paving robot operation monitoring system and method based on machine vision

Abstract

The embodiment of the invention provides a floor tile paving robot operation monitoring system and method based on machine vision, and belongs to the technical field of robot operation control. Floor tile bottom pictures are collected, the influence of floor tile defects on paving flatness is analyzed, tile glue data are collected, and the influence of tile glue overflow on paving flatness is analyzed; the method comprises the steps of collecting operation data of a floor tile paving robot, analyzing the influence of the operation state of the floor tile paving robot on the paving flatness, and analyzing the comprehensive flatness of floor tile paving based on the influence of floor tile defects on the paving flatness, the influence of tile glue overflow on the paving flatness and the influence of the operation state of the floor tile paving robot on the paving flatness. And whether the floor tiles meet the paving standard or not is analyzed based on the comprehensive flatness, and the floor tile paving flatness is comprehensively estimated by monitoring the floor tile defects, tile glue overflow and robot operation during floor tile paving, so that the floor tile paving effect estimation accuracy is improved, and the paving work is intelligentized and standardized.

Description

Technical Field

[0001] The present application relates to the field of robot operation control technology, and in particular to a floor tile laying robot operation monitoring system and method based on machine vision. Background Art

[0002] The floor tile laying robot is a high-tech automated equipment, mainly used for laying floor tiles in ground decoration projects. Through laser navigation technology, visual recognition technology, and elevation positioning system, it can achieve automatic walking, precise movement, and autonomous paving, and complete the integrated operations of tile adhesive laying, floor tile transportation, and floor tile laying construction. It is widely used in residential areas, high-speed rail stations, airports, office buildings, schools, and other scenes. The floor tile laying robot can improve construction efficiency, reduce labor intensity, and ensure paving quality.

[0003] In the prior art, the operation monitoring of floor tile laying robots is usually based on laser collection of deflection data, and then the posture of the working arm of the floor tile laying robot is adjusted in multiple directions to determine the laying angle and positioning during the paving process. However, in actual applications, since the laying quality is affected by the combined influence of floor tile production defects, tile adhesive overflow and the robot's operating status, the effect of floor tile laying cannot be accurately predicted based solely on the analysis of the paving position and paving angle. Therefore, how to improve the accuracy of floor tile paving estimation has become a technical problem that needs to be solved at present.

[0004] In order to solve the above problems, the present application provides a floor tile laying robot operation monitoring system and method based on machine vision. Summary of the invention

[0005] In view of the above problems, the purpose of this application is to provide a floor tile paving robot operation monitoring system and method based on machine vision. This application can timely analyze paving abnormalities and comprehensively estimate the flatness of floor tile paving by monitoring floor tile defects, tile adhesive overflow and robot operation during floor tile paving, thereby improving the accuracy of floor tile paving effect estimation and making paving work intelligent and standardized. The specific scheme is as follows: In a first aspect, the present application provides a method for monitoring the operation of a floor tile laying robot based on machine vision, comprising the following specific steps: Step 1: Collect pictures of the bottom of the floor tiles and analyze the impact of floor tile defects on paving flatness; Step 2: Collect tile adhesive data and analyze the impact of tile adhesive overflow on paving flatness; Step 3: Collect the operation data of the floor tile paving robot and analyze the influence of the operation status of the floor tile paving robot on the paving flatness; Step 4: Analyze the comprehensive flatness of floor tile paving based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of the operation state of the floor tile paving robot on paving flatness; Step 5: Analyze whether the floor tiles meet the laying standards based on the comprehensive flatness.

[0006] Optionally, the step 1 includes the following specific steps: Step S11, collecting a floor tile bottom image and setting it as a comparison image, obtaining a pre-collected standard floor tile bottom image, and outputting the comparison image and the standard floor tile bottom image in the form of a pixel value set; Step S12: Calculate the floor tile defect value based on the comparison image and the standard floor tile bottom image, and the floor tile defect value is calculated by the following formula: , where represents the tile defect value of the i-th tile, represents the pixel value set of the contrast image corresponding to the i-th floor tile, A set of pixel values ​​representing the bottom image of a standard floor tile. represents the intersection, represents a union; Step S13: Calculate the influence of the floor tile defect on the paving flatness based on the floor tile defect value. The influence of the floor tile defect on the paving flatness is calculated by the following formula: , where Indicates the impact of floor tile defects on paving flatness. Indicates the number of floor tiles.

[0007] Optionally, the step 2 includes the following specific steps: Step S21, collecting tile adhesive data, wherein the tile adhesive data includes the fluidity of the tile adhesive, and obtaining the weight of the floor tiles; Step S22, dividing the tile adhesive fluidity and floor tile weight data set into 80% training data set and 20% test data set, inputting 80% of the training data set into the tile adhesive overflow neural network analysis model for training and obtaining an initial tile adhesive overflow neural network analysis model, and then inputting 20% ​​of the test data set into the initial tile adhesive overflow neural network analysis model for testing, to obtain a tile adhesive overflow neural network analysis model with the highest tile adhesive overflow analysis accuracy; Step S23, obtaining the tile glue overflow amount based on the tile glue overflow amount neural network analysis model, the tile glue overflow amount neural network analysis model includes an output strategy formula of a specific neuron, and the output strategy formula of the specific neuron is: , where represents the output of the h-th neuron in the j+1th layer of the neural network analysis model for tile adhesive overflow, represents the connection weight of the j-th layer neuron r and the j+1-th layer h-th neuron in the neural network analysis model of tile adhesive overflow, represents the output of the j-th layer neuron r of the neural network analysis model of tile adhesive overflow, The offset of the linear relationship between the j-th layer neuron r and the j+1-th layer h-term neuron of the neural network analysis model for tile adhesive overflow, Represents the Sigmoid activation function; Step S24, calculating the influence value of the tile adhesive overflow on the paving flatness based on the tile adhesive overflow, wherein the influence value of the tile adhesive overflow on the paving flatness is calculated by the following formula: , where Indicates the influence of tile adhesive overflow on paving flatness. represents the amount of tile adhesive overflow corresponding to the paving of the i-th floor tile, Indicates the safe threshold for tile adhesive overflow.

[0008] Optionally, the step three includes the following specific steps: Step S31, collecting operation data of the floor tile paving robot, wherein the operation data of the floor tile paving robot includes vibration amplitude and vibration frequency; Step S32: Calculate the impact of the robot operation on the paving flatness based on the vibration amplitude and vibration frequency. The impact of the robot operation on the paving flatness is calculated by the following formula: , where Indicates the impact of robot operation on paving flatness. Indicates the robot operation monitoring time. represents the vibration frequency of the robot at time t, Indicates the standard vibration frequency of the robot operation. represents the vibration amplitude of the robot at time t, Indicates the standard vibration amplitude of the robot operation. represents the time integral.

[0009] Optionally, the step 4 includes the following specific steps: The comprehensive flatness of the floor tile paving is calculated based on the influence of floor tile defects on the paving flatness, the influence of tile adhesive overflow on the paving flatness, and the influence of robot operation on the paving flatness. The comprehensive flatness of the floor tile paving is calculated by the following formula: , where Indicates the overall flatness, represents the floor tile defect ratio, represents the coefficient of tile adhesive overflow, Indicates the robot operation ratio.

[0010] Optionally, the step five includes the following specific steps: Based on the comparison results between the comprehensive flatness and the preset comprehensive flatness standard value, it is analyzed whether the floor tiles meet the paving standards. If the comprehensive flatness is greater than or equal to the preset comprehensive flatness standard value, the output floor tiles meet the paving standards. If the comprehensive flatness is less than the preset comprehensive flatness standard value, the output floor tiles do not meet the paving standards.

[0011] In a second aspect, the present application provides a floor tile laying robot operation monitoring system based on machine vision, which is used to implement a floor tile laying robot operation monitoring method based on machine vision, including a visual acquisition module, which is used to collect floor tile bottom images as comparison images and standard floor tile bottom images, and output the comparison images and standard floor tile bottom images in the form of pixel value sets; A floor tile defect impact analysis module is used to calculate the floor tile defect value based on the comparison image and the standard floor tile bottom image, and calculate the impact value of the floor tile defect on the paving flatness based on the floor tile defect value; A data acquisition module is used to collect tile adhesive data, including tile adhesive fluidity, tile weight, and tile laying robot operation data, including vibration amplitude and vibration frequency; The tile adhesive overflow impact analysis module is used to train a tile adhesive overflow neural network analysis model based on the tile adhesive fluidity and floor tile weight data set, obtain the tile adhesive overflow based on the tile adhesive overflow neural network analysis model, and calculate the impact value of the tile adhesive overflow on the paving flatness based on the tile adhesive overflow; The robot operation impact analysis module is used to calculate the impact of the robot operation on the paving flatness based on the vibration amplitude and vibration frequency; Comprehensive flatness analysis module, used to calculate the comprehensive flatness of floor tile paving based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of robot operation on paving flatness; The standard judgment module is used to analyze whether the floor tiles meet the paving standards based on the comparison results between the comprehensive flatness and the preset comprehensive flatness standard value. If the comprehensive flatness is greater than or equal to the preset comprehensive flatness standard value, the output is that the floor tiles meet the paving standards. If the comprehensive flatness is less than the preset comprehensive flatness standard value, the output is that the floor tiles do not meet the paving standards.

[0012] In a third aspect, the present application provides a computer-readable storage medium, including computer operating instructions. When the computer operating instructions are executed on a computer, the computer executes the above-mentioned machine vision-based floor tile laying robot operation monitoring method.

[0013] Compared with the prior art, this application has the following beneficial effects: The present application collects pictures of the bottom of floor tiles, analyzes the impact of floor tile defects on paving smoothness, collects tile glue data, analyzes the impact of tile glue overflow on paving smoothness, collects floor tile paving robot operation data, analyzes the impact of the floor tile paving robot operation status on paving smoothness, analyzes the comprehensive smoothness of floor tile paving based on the impact of floor tile defects on paving smoothness, the impact of tile glue overflow on paving smoothness and the impact of the floor tile paving robot operation status on paving smoothness, and analyzes whether the floor tiles meet the paving standards based on the comprehensive flatness. The present application can timely analyze paving abnormalities and comprehensively estimate the flatness of floor tile paving by monitoring floor tile defects, tile glue overflow and robot operation during floor tile paving, thereby improving the accuracy of floor tile paving effect estimation and making paving work intelligent and standardized. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0015] Figure 1 This is a flow chart of the floor tile laying robot operation monitoring method based on machine vision in this application; Figure 2 This is a schematic diagram of step 1 of the process of the floor tile laying robot operation monitoring method based on machine vision in this application; Figure 3 This is a schematic diagram of step 2 of the process of the floor tile laying robot operation monitoring method based on machine vision in this application; Figure 4 This is a schematic diagram of step 3 of the process of the floor tile laying robot operation monitoring method based on machine vision in this application; Figure 5 This is a schematic diagram of the overall framework of the floor tile laying robot operation monitoring system based on machine vision in this application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0017] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way are interchangeable under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products or devices.

[0018] An analysis of the background technology shows that in actual applications, since the laying quality is affected by the combined effects of floor tile production defects, tile adhesive overflow and the operating status of the robot, the effect of floor tile laying cannot be accurately predicted based solely on the analysis of the laying position and laying angle. Therefore, how to improve the accuracy of floor tile laying prediction has become a technical problem that needs to be solved at present.

[0019] In view of the above problems, the present application provides a method for monitoring the operation of a floor tile laying robot based on machine vision, such as Figure 1 As shown, the floor tile laying robot operation monitoring method based on machine vision includes: Step 1: Collect pictures of the bottom of the floor tiles and analyze the impact of floor tile defects on paving flatness; like Figure 2 As shown, in this embodiment, step one includes the following specific steps: Step S11, collecting a floor tile bottom image and setting it as a comparison image, obtaining a pre-collected standard floor tile bottom image, and outputting the comparison image and the standard floor tile bottom image in the form of a pixel value set; In the specific implementation, before work, standard floor tiles without production defects are screened out, and the floor tile laying robot uses the visual system to collect the bottom image of the standard floor tile as a reference. During work, the floor tile laying robot uses the visual system to collect the bottom image of the floor tile when clamping the floor tile, and uses the image processing tool to traverse each pixel of the image, obtain each pixel value of the standard floor tile bottom image and the floor tile bottom image, and store them in a collection.

[0020] Step S12: Calculate the floor tile defect value based on the comparison image and the standard floor tile bottom image. The floor tile defect value is calculated using the following formula: , where Represents the floor tile defect value of the i-th floor tile. In this embodiment, the same pixel points of the comparison image and the standard floor tile bottom image are obtained by the intersection of the pixel value set of the comparison image and the pixel value set of the standard floor tile bottom image. The sum of the pixel points of the comparison image and the standard floor tile bottom image is obtained by the union of the pixel value set of the comparison image and the pixel value set of the standard floor tile bottom image. When the sum of the pixel points of the comparison image and the standard floor tile bottom image is determined, the more the same pixel points are, the more similar the comparison image and the standard floor tile bottom image are, and the smaller the corresponding floor tile defect value is. represents the pixel value set of the contrast image corresponding to the i-th floor tile, A set of pixel values ​​representing the bottom image of a standard floor tile. represents the intersection, represents a union; Step S13: Calculate the influence of floor tile defects on paving flatness based on the floor tile defect value. The influence of floor tile defects on paving flatness is calculated by the following formula: , where Indicates the influence of floor tile defects on paving flatness. In this embodiment, the influence of floor tile defects on paving flatness is reflected by the discrete degree of floor tile flatness. The higher the discrete degree of floor tile flatness, the greater the influence of floor tile defects on paving flatness. Indicates the number of floor tiles.

[0021] Step 2: Collect tile adhesive data and analyze the impact of tile adhesive overflow on paving flatness; like Figure 3 As shown, in this embodiment, step 2 includes the following specific steps: Step S21, collecting tile adhesive data, the tile adhesive data including the fluidity of the tile adhesive, and obtaining the weight of the floor tiles; In the specific implementation, a fluidity meter is used to measure the stirred tile adhesive to be used to obtain the fluidity of the tile adhesive. In order to ensure the accuracy of the fluidity, multiple measurements can be taken and the average value can be taken; the floor tile laying robot uses a weight sensor to collect the weight of the floor tiles when clamping the floor tiles.

[0022] Step S22, dividing the tile adhesive fluidity and floor tile weight data set into 80% training data set and 20% test data set, inputting 80% of the training data set into the tile adhesive overflow neural network analysis model for training and obtaining an initial tile adhesive overflow neural network analysis model, and then inputting 20% ​​of the test data set into the initial tile adhesive overflow neural network analysis model for testing, to obtain a tile adhesive overflow neural network analysis model with the highest tile adhesive overflow analysis accuracy; Step S23, obtaining the tile glue overflow amount based on the tile glue overflow amount neural network analysis model, the tile glue overflow amount neural network analysis model includes an output strategy formula of a specific neuron, and the output strategy formula of the specific neuron is: , where represents the output of the h-th neuron in the j+1th layer of the neural network analysis model for tile adhesive overflow, represents the connection weight of the j-th layer neuron r and the j+1-th layer h-th neuron in the neural network analysis model of tile adhesive overflow, represents the output of the j-th layer neuron r of the neural network analysis model of tile adhesive overflow, The offset of the linear relationship between the j-th layer neuron r and the j+1-th layer h-term neuron of the neural network analysis model for tile adhesive overflow, Represents the Sigmoid activation function; Step S24: Calculate the influence of the tile adhesive overflow on the paving flatness based on the tile adhesive overflow. The influence of the tile adhesive overflow on the paving flatness is calculated by the following formula: , where Indicates the influence of tile adhesive overflow on paving flatness. represents the amount of tile adhesive overflow corresponding to the paving of the i-th floor tile, It represents the safety threshold of tile adhesive overflow. In specific implementation, the safety threshold of tile adhesive overflow is obtained through floor tile laying experiment. Several floor tiles are laid, and the tile adhesive overflow during laying is recorded. The area that obviously affects the flatness is screened out, the tile adhesive overflow in the area is counted and output as a set, and the minimum value of the tile adhesive overflow in the set is selected as the safety threshold of the tile adhesive overflow.

[0023] Step 3: Collect the operation data of the floor tile paving robot and analyze the influence of the operation status of the floor tile paving robot on the paving flatness; like Figure 4 As shown, in this embodiment, step three includes the following specific steps: Step S31, collecting operation data of the floor tile paving robot, where the operation data of the floor tile paving robot includes vibration amplitude and vibration frequency; Step S32: Calculate the impact of the robot operation on the paving flatness based on the vibration amplitude and the vibration frequency. The impact of the robot operation on the paving flatness is calculated by the following formula: , where Indicates the impact of robot operation on paving flatness. Indicates the robot operation monitoring time. represents the vibration frequency of the robot at time t, Indicates the standard vibration frequency of the robot operation. represents the vibration amplitude of the robot at time t, Indicates the standard vibration amplitude of the robot operation. Represents time integral. In this embodiment, the larger the vibration frequency and the vibration amplitude, the more serious the fault of the floor tile paving robot, and the greater the impact of the robot operation on the paving flatness. In the specific implementation, the standard vibration frequency of the robot operation and the standard vibration amplitude of the robot operation are obtained through the robot operation experiment, and several floor tile paving robots that meet the production qualification standards are selected for operation monitoring, and the vibration frequency and vibration amplitude of the robot during operation are recorded. The average values ​​of the collected vibration frequency and vibration amplitude are taken as the standard vibration frequency of the robot operation and the standard vibration amplitude of the robot operation.

[0024] Step 4: Analyze the comprehensive flatness of floor tile paving based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of the operation state of the floor tile paving robot on paving flatness; In this embodiment, step 4 includes the following specific steps: The comprehensive flatness of floor tile paving is calculated based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of robot operation on paving flatness. The comprehensive flatness of floor tile paving is calculated by the following formula: , where Indicates the overall flatness, represents the floor tile defect ratio, represents the coefficient of tile adhesive overflow, Indicates the robot operation ratio.

[0025] In the specific implementation, the floor tile defect ratio coefficient, the tile glue overflow ratio coefficient and the robot operation ratio coefficient are obtained through fitting software, several areas are selected for floor tile paving, pictures of the bottom of the floor tiles are collected, the influence of floor tile defects on the paving smoothness is analyzed, tile glue data is collected, the influence of tile glue overflow on the paving smoothness is analyzed, the operation data of the floor tile paving robot is collected, and the influence of the operation state of the floor tile paving robot on the paving smoothness is analyzed. The comprehensive flatness of the area is obtained based on the influence of floor tile defects on the paving smoothness, the influence of tile glue overflow on the paving smoothness and the influence of the operation state of the floor tile paving robot on the paving smoothness. After the actual floor tile paving is carried out, the comprehensive flatness of the area is obtained again, and the comprehensive flatness obtained twice is input into the fitting software to output the floor tile defect ratio coefficient, tile glue overflow ratio coefficient and robot operation ratio coefficient corresponding to the comprehensive flatness with the highest accuracy and used as the values ​​of the floor tile defect ratio coefficient, tile glue overflow ratio coefficient and robot operation ratio coefficient in this embodiment.

[0026] Step 5: Analyze whether the floor tiles meet the laying standards based on the comprehensive flatness.

[0027] In this embodiment, step five includes the following specific steps: Based on the comparison results between the comprehensive flatness and the preset comprehensive flatness standard value, it is analyzed whether the floor tiles meet the paving standards. If the comprehensive flatness is greater than or equal to the preset comprehensive flatness standard value, the output floor tiles meet the paving standards. If the comprehensive flatness is less than the preset comprehensive flatness standard value, the output floor tiles do not meet the paving standards.

[0028] like Figure 5 As shown, the embodiment of the present application provides a floor tile laying robot operation monitoring system based on machine vision, which is implemented based on the above-mentioned floor tile laying robot operation monitoring method based on machine vision, including a visual acquisition module for collecting floor tile bottom images as comparison images and standard floor tile bottom images, and outputting the comparison images and the standard floor tile bottom images in the form of a pixel value set; A floor tile defect impact analysis module is used to calculate the floor tile defect value based on the comparison image and the standard floor tile bottom image, and calculate the impact value of the floor tile defect on the paving flatness based on the floor tile defect value; A data acquisition module is used to collect tile adhesive data, including tile adhesive fluidity, tile weight, and tile laying robot operation data, including vibration amplitude and vibration frequency; The tile adhesive overflow impact analysis module is used to train a tile adhesive overflow neural network analysis model based on the tile adhesive fluidity and floor tile weight data set, obtain the tile adhesive overflow based on the tile adhesive overflow neural network analysis model, and calculate the impact value of the tile adhesive overflow on the paving flatness based on the tile adhesive overflow; The robot operation impact analysis module is used to calculate the impact of the robot operation on the paving flatness based on the vibration amplitude and vibration frequency; Comprehensive flatness analysis module, used to calculate the comprehensive flatness of floor tile paving based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of robot operation on paving flatness; The standard judgment module is used to analyze whether the floor tiles meet the paving standards based on the comparison results between the comprehensive flatness and the preset comprehensive flatness standard value. If the comprehensive flatness is greater than or equal to the preset comprehensive flatness standard value, the output is that the floor tiles meet the paving standards. If the comprehensive flatness is less than the preset comprehensive flatness standard value, the output is that the floor tiles do not meet the paving standards.

[0029] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the above-mentioned machine vision-based floor tile laying robot operation monitoring method is implemented.

[0030] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0031] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0032] The above is a detailed introduction to the technical contents provided by the present application. Specific examples are used in the present application to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the contents of this specification should not be understood as limiting the present application.

Claims

1. A floor tile laying robot operation monitoring method based on machine vision, characterized in that: The specific steps include: Step 1: Collect pictures of the bottom of the floor tiles and analyze the impact of floor tile defects on paving flatness; Step 2: Collect tile adhesive data and analyze the impact of tile adhesive overflow on paving flatness; Step 3: Collect the operation data of the floor tile paving robot and analyze the influence of the operation status of the floor tile paving robot on the paving flatness; Step 4: Analyze the comprehensive flatness of floor tile paving based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of the operation state of the floor tile paving robot on paving flatness; Step 5: Analyze whether the floor tiles meet the laying standards based on the comprehensive flatness.

2. The machine vision-based floor tile paving robot operation monitoring method according to claim 1, characterized in that: The step 1 includes the following specific steps: Step S11, collecting a floor tile bottom image and setting it as a comparison image, obtaining a pre-collected standard floor tile bottom image, and outputting the comparison image and the standard floor tile bottom image in the form of a pixel value set; Step S12: Calculate the floor tile defect value based on the comparison image and the standard floor tile bottom image, and the floor tile defect value is calculated by the following formula: , where represents the tile defect value of the i-th tile, represents the pixel value set of the contrast image corresponding to the i-th floor tile, A set of pixel values ​​representing the bottom image of a standard floor tile. represents the intersection, represents a union; Step S13: Calculate the influence of the floor tile defect on the paving flatness based on the floor tile defect value. The influence of the floor tile defect on the paving flatness is calculated by the following formula: , where Indicates the impact of floor tile defects on paving flatness. Indicates the number of floor tiles.

3. The machine vision-based floor tile paving robot operation monitoring method according to claim 2, characterized in that: The step 2 includes the following specific steps: Step S21, collecting tile adhesive data, wherein the tile adhesive data includes the fluidity of the tile adhesive, and obtaining the weight of the floor tiles; Step S22, dividing the tile adhesive fluidity and floor tile weight data set into 80% training data set and 20% test data set, inputting 80% of the training data set into the tile adhesive overflow neural network analysis model for training and obtaining an initial tile adhesive overflow neural network analysis model, and then inputting 20% ​​of the test data set into the initial tile adhesive overflow neural network analysis model for testing, to obtain a tile adhesive overflow neural network analysis model with the highest tile adhesive overflow analysis accuracy; Step S23, obtaining the tile glue overflow amount based on the tile glue overflow amount neural network analysis model, the tile glue overflow amount neural network analysis model includes an output strategy formula of a specific neuron, and the output strategy formula of the specific neuron is: , where represents the output of the h-th neuron in the j+1th layer of the neural network analysis model for tile adhesive overflow, represents the connection weight of the j-th layer neuron r and the j+1-th layer h-th neuron in the neural network analysis model of tile adhesive overflow, represents the output of the j-th layer neuron r of the neural network analysis model of tile adhesive overflow, The offset of the linear relationship between the j-th layer neuron r and the j+1-th layer h-term neuron of the neural network analysis model for tile adhesive overflow, Represents the Sigmoid activation function; Step S24, calculating the influence value of the tile adhesive overflow on the paving flatness based on the tile adhesive overflow, wherein the influence value of the tile adhesive overflow on the paving flatness is calculated by the following formula: , where Indicates the influence of tile adhesive overflow on paving flatness. represents the amount of tile adhesive overflow corresponding to the ith floor tile after paving, Indicates the safe threshold for tile adhesive overflow.

4. The machine vision-based floor tile paving robot operation monitoring method according to claim 3, characterized in that: The step three includes the following specific steps: Step S31, collecting operation data of the floor tile paving robot, wherein the operation data of the floor tile paving robot includes vibration amplitude and vibration frequency; Step S32: Calculate the impact of the robot operation on the paving flatness based on the vibration amplitude and vibration frequency. The impact of the robot operation on the paving flatness is calculated by the following formula: , where Indicates the impact of robot operation on paving flatness. Indicates the robot operation monitoring time. represents the vibration frequency of the robot at time t, Indicates the standard vibration frequency of the robot operation. represents the vibration amplitude of the robot at time t, Indicates the standard vibration amplitude of the robot operation. represents the time integral.

5. The machine vision-based floor tile paving robot operation monitoring method as claimed in claim 4, characterized in that: The step 4 includes the following specific steps: The comprehensive flatness of the floor tile paving is calculated based on the influence of floor tile defects on the paving flatness, the influence of tile adhesive overflow on the paving flatness, and the influence of robot operation on the paving flatness. The comprehensive flatness of the floor tile paving is calculated by the following formula: , where Indicates the overall flatness, represents the floor tile defect ratio, represents the coefficient of tile adhesive overflow, Indicates the robot operation ratio.

6. The machine vision-based floor tile paving robot operation monitoring method according to claim 5, characterized in that: The step five includes the following specific steps: Based on the comparison results between the comprehensive flatness and the preset comprehensive flatness standard value, it is analyzed whether the floor tiles meet the paving standards. If the comprehensive flatness is greater than or equal to the preset comprehensive flatness standard value, the output floor tiles meet the paving standards. If the comprehensive flatness is less than the preset comprehensive flatness standard value, the output floor tiles do not meet the paving standards.

7. A floor tile laying robot operation monitoring system based on machine vision, used to implement the floor tile laying robot operation monitoring method based on machine vision as claimed in any one of claims 1 to 6, characterized in that: It includes a visual acquisition module, which is used to collect the bottom image of the floor tile and set it as a comparison image and a standard bottom image of the floor tile, and output the comparison image and the standard bottom image of the floor tile in the form of a pixel value set; A floor tile defect impact analysis module is used to calculate the floor tile defect value based on the comparison image and the standard floor tile bottom image, and calculate the impact value of the floor tile defect on the paving flatness based on the floor tile defect value; A data acquisition module is used to collect tile adhesive data, including tile adhesive fluidity, tile weight, and tile laying robot operation data, including vibration amplitude and vibration frequency; The tile adhesive overflow impact analysis module is used to train a tile adhesive overflow neural network analysis model based on the tile adhesive fluidity and floor tile weight data set, obtain the tile adhesive overflow based on the tile adhesive overflow neural network analysis model, and calculate the impact value of the tile adhesive overflow on the paving flatness based on the tile adhesive overflow; The robot operation impact analysis module is used to calculate the impact of the robot operation on the paving flatness based on the vibration amplitude and vibration frequency; Comprehensive flatness analysis module, used to calculate the comprehensive flatness of floor tile paving based on the influence of floor tile defects on paving flatness, the influence of tile adhesive overflow on paving flatness, and the influence of robot operation on paving flatness; The standard judgment module is used to analyze whether the floor tiles meet the paving standards based on the comparison results between the comprehensive flatness and the preset comprehensive flatness standard value. If the comprehensive flatness is greater than or equal to the preset comprehensive flatness standard value, the output is that the floor tiles meet the paving standards. If the comprehensive flatness is less than the preset comprehensive flatness standard value, the output is that the floor tiles do not meet the paving standards.

8. A computer-readable storage medium, characterized in that: It includes computer operating instructions. When the computer operating instructions are executed on a computer, the computer executes the machine vision-based floor tile paving robot operation monitoring method as described in any one of claims 1 to 6.

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