Glass gluing planning method and system based on artificial intelligence

Abstract

The invention discloses a glass gluing planning method and system based on artificial intelligence, and belongs to the technical field of gluing planning. The method is used for solving the technical problems that in an existing scheme, the glass gluing autonomous supervision effect is poor, and the glass gluing planning autonomous management and control effect is poor. Monitoring processing and data analysis are carried out from the internal dimension of gluing; the abnormal influence sequence obtained through earlier stage processing is further processed and analyzed, and the external overall abnormal influence corresponding to the gluing range is determined, so that periodic digital processing and analysis on the gluing external state from the gluing external dimension can be realized; meanwhile, reliable gluing external dimension supervision data support can be provided for evaluation and dynamic management and control of a subsequent gluing overall state; the local supervision state of the glass gluing in the supervision period is integrally evaluated by integrating the gluing monitoring analysis data of different dimensions, and the subsequent glass gluing is dynamically controlled in a self-adaptive manner according to the integral evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of gluing planning, and in particular to a glass gluing planning method and system based on artificial intelligence. Background Art

[0002] Glass gluing planning refers to the process of applying a layer of glue on the surface or edge of the glass in order to achieve specific functions or aesthetic requirements during the processing of glass products.

[0003] The existing glass gluing planning scheme has certain defects in implementation. It is unable to implement periodic gluing quality supervision and analysis on the automatic glass gluing process, and adaptively and dynamically control the subsequent gluing planning according to the supervision and analysis results to avoid unqualified glass gluing schemes from having greater negative impacts. There are technical problems such as poor autonomous supervision of glass gluing and poor autonomous control of glass gluing planning. Summary of the invention

[0004] The purpose of the present invention is to provide a glass gluing planning method and system based on artificial intelligence, which is used to solve the technical problems of poor autonomous supervision of glass gluing and poor autonomous control of glass gluing planning in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions: Glass gluing planning method based on artificial intelligence, including: Acquire a colloid image corresponding to the coated glass colloid according to a preset supervision distance; Preprocessing and feature extraction of colloid images to obtain corresponding colloid features, and feature processing of colloid features and implementation of multi-dimensional colloid coating quality monitoring and analysis to obtain a quality monitoring and analysis set; the quality monitoring and analysis set consists of internal monitoring and analysis data of coating and external monitoring and analysis data of coating; The local supervision status of the glass gluing corresponding to the supervision distance is determined according to the monitoring and analysis data of different dimensions in the quality monitoring and analysis set, and the subsequent glass gluing is adaptively and dynamically controlled according to the local supervision status of the glass gluing.

[0006] Preferably, when monitoring and analyzing the internal dimensions of the colloid features, the colloid features are pixel-identified, and the total number of pixel types identified and the corresponding pixel proportions are counted; Perform data analysis on the total number of pixel types, and if the total number of pixel types is 1, generate a normal instruction inside the first glue coating; If the total number of pixel types is not 1, an internal verification instruction is generated and the internal abnormality degree is obtained through formula calculation.

[0007] Preferably, when determining the internal states of the glue coating corresponding to the plurality of pixel types according to the internal abnormality degree, if the internal abnormality degree is 0, a second internal normal instruction of the glue coating is generated; Otherwise, a glue internal exception instruction is generated; The first normal instruction inside the glue coating, the second normal instruction inside the glue coating or the abnormal instruction inside the glue coating constitute the internal monitoring and analysis data of the glue coating.

[0008] Preferably, the calculation formula of the internal abnormality NY is: ; In the formula, Fj is the pixel ratio corresponding to different pixel types; j is a different pixel type, j=1, 2, 3, ..., m; m is a positive integer; U is the standard pixel ratio range; M is the total number that meets the corresponding conditions.

[0009] Preferably, when monitoring and analyzing the external dimension of the colloid feature, four vertices corresponding to the colloid feature are obtained, and adjacent vertices are connected to obtain a contour map of the colloid feature; Identify the fitting state between all vertex lines and the colloid in the colloid characteristic contour map, if there is a gap between the colloid and the line in the colloid characteristic contour map, generate a first abnormal instruction, obtain the area of ​​the gap according to the first abnormal instruction and mark it as a first abnormal value; If the colloid in the colloid characteristic contour diagram exceeds the connecting line, a second abnormal instruction is generated, and the area corresponding to the part exceeding the connecting line is obtained according to the second abnormal instruction and marked as a second abnormal value; If the colloid in the colloid feature contour map fits the connecting line, a normal instruction is generated.

[0010] Preferably, the abnormal influence degree corresponding to the colloid feature is obtained by calculating the abnormal influence formula according to the first abnormal instruction or the second abnormal instruction; Among them, the expression of the abnormal impact formula is ; Wherein, k=1, 2; YYk is YY1 and YY2, which are the first abnormal influence and the second abnormal influence respectively; Sk is S1 and S2, which are the first abnormal value and the second abnormal value respectively; Ski is a different first abnormal value or second abnormal value; i=1, 2, 3, ..., N; N is a positive integer; Nk is N1 and N2, which are the total number of first abnormal values ​​and the total number of second abnormal values ​​respectively; A is the standard abnormal influence value; The first abnormality impact degree and the second abnormality impact degree obtained by calculation are sorted and combined to obtain an abnormality impact sequence.

[0011] Preferably, when determining the overall abnormal impact of the glue coating range according to the abnormal impact sequence, the abnormal impact sequence is traversed and analyzed. If the values ​​in the abnormal impact sequence are all 0, a verification instruction is generated, and the formula is used according to the verification instruction. Calculate and obtain the abnormal impact integration degree YZ; if the abnormal impact integration degree is 0, generate the external normal instruction of gluing; If the abnormal impact integration is not 0, the first glue external abnormal instruction is generated; If there is a value in the abnormal impact sequence that is not 0, a second glue coating external abnormal instruction or a third glue coating external abnormal instruction is generated according to the abnormal type corresponding to the element whose value is not 0; The normal instruction of the external glue coating, the first abnormal instruction of the external glue coating, the second abnormal instruction of the external glue coating or the third abnormal instruction of the external glue coating constitutes the external monitoring and analysis data of the glue coating.

[0012] Preferably, the internal monitoring and analysis data of the gluing and the external monitoring and analysis data of the gluing in the quality monitoring and analysis set are traversed to determine the local monitoring state of the glass gluing corresponding to the corresponding monitoring distance; If all the traversal results are normal instructions, then an overall normal instruction for gluing is generated and the subsequent glass gluing is controlled to continue with the preset parameters.

[0013] Preferably, if there are abnormal instructions in the traversal results, an overall abnormal instruction for gluing is generated and the subsequent glass gluing is controlled to be suspended, and the abnormal data of the corresponding dimension is optimized and managed according to the abnormal instruction.

[0014] Glass glue coating planning system based on artificial intelligence, including: The feature extraction processing and analysis module is used to pre-process the colloid image and extract the features to obtain the corresponding colloid features, perform feature processing on the colloid features and implement multi-dimensional colloid coating quality monitoring and analysis to obtain a quality monitoring and analysis set; the quality monitoring and analysis set is composed of internal monitoring and analysis data of the coating and external monitoring and analysis data of the coating; The multi-dimensional analysis result control module is used to determine the local supervision status of the glass glue coating corresponding to the supervision distance according to the monitoring and analysis data of different dimensions in the quality monitoring and analysis set, and to adaptively and dynamically control the subsequent glass glue coating according to the local supervision status of the glass glue coating.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention performs monitoring and data analysis from the internal dimension of the glue coating, which can not only realize the periodic digital processing and analysis of the internal state of the glue coating from the internal dimension of the glue coating, but also provide reliable internal dimension supervision data support for the subsequent evaluation and dynamic control of the overall state of the glue coating.

[0016] The present invention further processes and analyzes the abnormal impact sequence obtained in the previous processing to determine the external overall abnormal impact corresponding to the gluing range, which can not only realize the periodic digital processing and analysis of the external state of the gluing from the external dimension of the gluing, but also provide reliable external dimension supervision data support for the subsequent evaluation of the overall state of the gluing and dynamic control; it improves the diversity of the periodic supervision analysis of glass gluing and the reliability and comprehensiveness of the subsequent expansion and utilization.

[0017] The present invention integrates glue coating monitoring and analysis data of different dimensions to comprehensively evaluate the local supervision status of glass glue coating in the corresponding supervision cycle, and adaptively and dynamically controls the subsequent glass glue coating according to the overall evaluation results, thereby improving the autonomous supervision effect of glass glue coating and the autonomous control effect of glass glue coating planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings.

[0019] Figure 1 The present invention is a flowchart of the glass gluing planning method based on artificial intelligence.

[0020] Figure 2 This is a module block diagram of the glass gluing planning system based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1: Figure 1 As shown, the present invention is a glass gluing planning method based on artificial intelligence, comprising: Obtain the planned path corresponding to the glass gluing and the corresponding planned gluing data; Among them, the planning path is determined according to the glued glass shape and glue coating design corresponding to the glued glass; The planned gluing data includes the gluing coordinates at different positions, as well as the unit gluing speed and unit gluing amount corresponding to different gluing coordinates; the unit gluing speed and unit gluing amount can be determined according to the specific glass and the actual gluing design requirements; the gluing coordinates can be determined according to the center point of the glass to be glued, the preset coordinate axis direction and the coordinate spacing; When the glass glue coating starts, a colloid image corresponding to the glued glass colloid is obtained according to a preset supervision distance; The supervision distance is determined based on the total length of the planned path and the actual glue coating supervision requirements, and the unit is centimeters; Colloid images can be acquired using high-resolution industrial cameras or machine vision systems; Preprocessing and feature extraction of colloid images to obtain corresponding colloid features, and feature processing of colloid features and implementation of multi-dimensional colloid coating quality monitoring and analysis to obtain a quality monitoring analysis set; Among them, the preprocessing and feature extraction of the colloid image are both existing conventional technical solutions, and the specific steps are not repeated here; the colloid feature is the colloid after the glue is applied; In addition, the feature processing of the colloid features includes the processing of the inner dimension of the glue coating and the outer dimension of the glue coating; Specifically, when monitoring and analyzing the internal dimensions of the colloid characteristics, the colloid characteristics are pixel-recognized, and the total number of pixel types identified and the corresponding pixel ratios are counted; the pixel types correspond to different colors; the pixel ratio is the ratio between the corresponding pixel value and the overall pixel value of the colloid; Perform data analysis on the total number of pixel types. If the total number of pixel types is 1, a normal instruction is generated for the first glue coating. The corresponding scene may be that there is no noise or bubbles in the colloid feature. If the total number of pixel types is not 1, an internal verification instruction is generated and the internal abnormality NY is calculated by formula; the calculation formula of the internal abnormality NY is: ; Wherein, Fj is the pixel ratio corresponding to different pixel types; j is different pixel types, j=1, 2, 3, ..., m; m is a positive integer; U is the standard pixel ratio range, which is determined according to the colloid color data corresponding to the glass coating and the noise pixel data of the previous image processing; M is the total number that meets the corresponding conditions; It should be noted that the internal abnormality is used to integrate and calculate the pixel identification data obtained from the monitoring from the internal dimension of the glue coating to digitally represent the internal state of the glue coating of its colloid characteristics; When determining the internal states of the glue coating corresponding to several pixel types according to the internal abnormality degree, if the internal abnormality degree is 0, a second internal normal instruction of the glue coating is generated; Otherwise, a glue internal exception instruction is generated; The first normal instruction inside the glue coating, the second normal instruction inside the glue coating or the abnormal instruction inside the glue coating constitute the internal monitoring and analysis data of the glue coating; In the embodiment of the present invention, by monitoring and processing and data analysis from the internal dimension of the glue coating, it is possible to achieve periodic digital processing and analysis of the internal state of the glue coating from the internal dimension of the glue coating, and at the same time provide reliable internal dimension supervision data support for the subsequent evaluation of the overall state of the glue coating and dynamic control; When monitoring and analyzing the external dimension of the colloid feature, four vertices corresponding to the colloid feature are obtained, and adjacent vertices are connected to obtain a contour map of the colloid feature; the shape corresponding to the colloid feature is a rectangle; Identify the fitting state between all vertex lines and the colloid in the colloid characteristic contour map, if there is a gap between the colloid and the line in the colloid characteristic contour map, generate a first abnormal instruction, obtain the area of ​​the gap according to the first abnormal instruction and mark it as a first abnormal value; If the colloid in the colloid characteristic contour diagram exceeds the connecting line, a second abnormal instruction is generated, and the area corresponding to the part exceeding the connecting line is obtained according to the second abnormal instruction and marked as a second abnormal value; If the colloid in the colloid feature contour map fits the connecting line, a normal instruction is generated; Among them, the gap, protrusion or fit between the colloid and the connecting line in the colloid feature contour map and the corresponding area can be obtained by feature detection through existing convolutional neural networks (CNNs), and the specific implementation steps are not repeated here; According to the first abnormal instruction or the second abnormal instruction, the abnormal influence degree corresponding to the colloid feature is calculated by using the abnormal influence formula; Among them, the expression of the abnormal impact formula is ; In the formula, k=1, 2; YYk is YY1 and YY2, which are the first abnormal influence and the second abnormal influence respectively; Sk is S1 and S2, which are the first abnormal value and the second abnormal value respectively; Ski is a different first abnormal value or second abnormal value; i=1, 2, 3, ..., N; N is a positive integer; Nk is N1 and N2, which are the total number of the first abnormal value and the total number of the second abnormal value respectively; A is the standard abnormal influence value, which can be determined according to the previous glass glue test data; The first abnormal impact degree and the second abnormal impact degree obtained by calculation are sorted and combined to obtain an abnormal impact sequence; In the embodiment of the present invention, by monitoring and analyzing the external dimension of the glue coating on the colloid characteristics, the fitting state between the colloid and the connecting line in the colloid characteristic contour diagram is monitored, digitally processed and combined to obtain an abnormal impact sequence; a variety of external monitoring data processing can be performed on the colloid characteristics, and reliable digital data support can be provided for the subsequent state analysis of the external dimension of the glue coating; When determining the overall abnormal impact of the gluing range according to the abnormal impact sequence, the abnormal impact sequence is traversed and analyzed. If the values ​​in the abnormal impact sequence are all 0, a verification instruction is generated, and the formula is used according to the verification instruction. Calculate and obtain the abnormal impact integration degree YZ; if the abnormal impact integration degree is 0, generate the external normal instruction of gluing; If the abnormal impact integration is not 0, the first glue external abnormal instruction is generated; If there is a value in the abnormal impact sequence that is not 0, a second glue coating external abnormal instruction or a third glue coating external abnormal instruction is generated according to the abnormal type corresponding to the element whose value is not 0; The gluing external normal instruction, the first gluing external abnormal instruction, the second gluing external abnormal instruction or the third gluing external abnormal instruction constitutes the gluing external monitoring and analysis data; In the embodiment of the present invention, by further processing and analyzing the abnormal impact sequence obtained in the previous processing, the external overall abnormal impact corresponding to the gluing range is determined, which can not only realize the periodic digital processing and analysis of the external state of the gluing from the external dimension of the gluing, but also provide reliable external dimension supervision data support for the subsequent evaluation of the overall state of the gluing and dynamic control; it improves the diversity of the periodic supervision analysis of glass gluing and the reliability and comprehensiveness of the subsequent expansion and utilization; The internal monitoring and analysis data of gluing and the external monitoring and analysis data of gluing constitute the quality monitoring and analysis set; Determine the local supervision state of glass gluing corresponding to the supervision distance according to the monitoring and analysis data of different dimensions in the quality monitoring and analysis set, and dynamically control the subsequent glass gluing according to the local supervision state of glass gluing; The internal monitoring and analysis data of gluing and the external monitoring and analysis data of gluing in the quality monitoring and analysis set are traversed to determine the local supervision status of glass gluing corresponding to the supervision distance; If all the traversed results are normal instructions, then a normal instruction for gluing is generated and the subsequent glass gluing is controlled to continue gluing with preset parameters; It should be noted that, in the embodiment of the present invention, only when the monitoring and analysis data of different aspects of gluing are normal, the subsequent gluing of the planned gluing data will be continued; if they are not present at the same time, targeted optimization management will be implemented for the planned gluing data; If there are abnormal instructions in the traversal results, an overall abnormal instruction for gluing is generated and the subsequent glass gluing is controlled to be suspended, and the abnormal data of the corresponding dimension is optimized and managed according to the abnormal instruction; Among them, when performing optimization management, targeted management can be implemented for the anomalies in the dimension to which the abnormal instructions belong, such as optimizing the planned gluing data from the inside of the gluing according to the internal abnormal instructions of the gluing, and optimizing the planned gluing data from the outside of the gluing according to the first external abnormal instructions of the gluing, the second external abnormal instructions of the gluing or the third external abnormal instructions of the gluing.

[0023] In the embodiment of the present invention, the local supervision status of glass gluing in the supervision cycle is comprehensively evaluated by integrating the glue monitoring and analysis data of different dimensions, and the subsequent glass gluing is adaptively and dynamically controlled according to the overall evaluation result, thereby improving the autonomous supervision effect of glass gluing and the autonomous control effect of glass gluing planning.

[0024] Example 2: Figure 2 As shown, the present invention is a glass gluing planning system based on artificial intelligence, comprising: The feature extraction processing and analysis module is used to pre-process the colloid image and extract the features to obtain the corresponding colloid features, perform feature processing on the colloid features and implement multi-dimensional colloid coating quality monitoring and analysis to obtain a quality monitoring and analysis set; the quality monitoring and analysis set is composed of internal monitoring and analysis data of the coating and external monitoring and analysis data of the coating; The multi-dimensional analysis result control module is used to determine the local supervision status of the glass glue coating corresponding to the supervision distance according to the monitoring and analysis data of different dimensions in the quality monitoring and analysis set, and to adaptively and dynamically control the subsequent glass glue coating according to the local supervision status of the glass glue coating.

[0025] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0026] In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0027] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0028] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0029] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. The glass gluing planning method based on artificial intelligence is characterized by: include: Acquire a colloid image corresponding to the coated glass colloid according to a preset supervision distance; Preprocessing and feature extraction of colloid images to obtain corresponding colloid features, and feature processing of colloid features and implementation of multi-dimensional colloid coating quality monitoring and analysis to obtain a quality monitoring and analysis set; the quality monitoring and analysis set consists of internal monitoring and analysis data of coating and external monitoring and analysis data of coating; The local supervision status of the glass gluing corresponding to the supervision distance is determined according to the monitoring and analysis data of different dimensions in the quality monitoring and analysis set, and the subsequent glass gluing is adaptively and dynamically controlled according to the local supervision status of the glass gluing.

2. The glass gluing planning method based on artificial intelligence according to claim 1 is characterized in that: When monitoring and analyzing the internal dimensions of the colloid features, the colloid features are pixel-identified, and the total number of pixel types identified and the corresponding pixel proportions are counted; Perform data analysis on the total number of pixel types, and if the total number of pixel types is 1, generate a normal instruction inside the first glue coating; If the total number of pixel types is not 1, an internal verification instruction is generated and the internal abnormality degree is obtained through formula calculation.

3. The glass gluing planning method based on artificial intelligence according to claim 2 is characterized in that: When determining the internal states of the glue coating corresponding to several pixel types according to the internal abnormality degree, if the internal abnormality degree is 0, a second internal normal instruction of the glue coating is generated; Otherwise, a glue internal exception instruction is generated; The first normal instruction inside the glue coating, the second normal instruction inside the glue coating or the abnormal instruction inside the glue coating constitute the internal monitoring and analysis data of the glue coating.

4. The glass gluing planning method based on artificial intelligence according to claim 2 is characterized in that: The calculation formula of internal abnormality NY is: ; Where Fj is the pixel proportion corresponding to different pixel types; j is different pixel types, j=1, 2, 3, ..., m; m is a positive integer; U is the standard pixel ratio range; M is the total number that meets the corresponding conditions.

5. The glass gluing planning method based on artificial intelligence according to claim 1 is characterized in that: When monitoring and analyzing the external dimension of the colloid feature, four vertices corresponding to the colloid feature are obtained, and adjacent vertices are connected to obtain a contour map of the colloid feature; Identify the fitting state between all vertex lines and the colloid in the colloid characteristic contour map, if there is a gap between the colloid and the line in the colloid characteristic contour map, generate a first abnormal instruction, obtain the area of ​​the gap according to the first abnormal instruction and mark it as a first abnormal value; If the colloid in the colloid characteristic contour diagram exceeds the connecting line, a second abnormal instruction is generated, and the area corresponding to the part exceeding the connecting line is obtained according to the second abnormal instruction and marked as a second abnormal value; If the colloid in the colloid feature contour map fits the connecting line, a normal instruction is generated.

6. The glass gluing planning method based on artificial intelligence according to claim 5 is characterized in that: According to the first abnormal instruction or the second abnormal instruction, the abnormal influence degree corresponding to the colloid feature is calculated by using the abnormal influence formula; Among them, the expression of the abnormal impact formula is ; Wherein, k=1, 2; YYk is YY1 and YY2, which are the first abnormal influence and the second abnormal influence respectively; Sk is S1 and S2, which are the first abnormal value and the second abnormal value respectively; Ski is a different first abnormal value or second abnormal value; i=1, 2, 3, ..., N; N is a positive integer; Nk is N1 and N2, which are the total number of first abnormal values ​​and the total number of second abnormal values ​​respectively; A is the standard abnormal influence value; The first abnormality impact degree and the second abnormality impact degree obtained by calculation are sorted and combined to obtain an abnormality impact sequence.

7. The glass gluing planning method based on artificial intelligence according to claim 6 is characterized in that: When determining the overall abnormal impact of the gluing range according to the abnormal impact sequence, the abnormal impact sequence is traversed and analyzed. If the values ​​in the abnormal impact sequence are all 0, a verification instruction is generated, and the formula is used according to the verification instruction. Calculate and obtain the abnormal impact integration degree YZ; If the abnormal impact integration is 0, a glue-coated external normal instruction is generated; If the abnormal impact integration is not 0, the first glue external abnormal instruction is generated; If there is a value in the abnormal impact sequence that is not 0, a second glue coating external abnormal instruction or a third glue coating external abnormal instruction is generated according to the abnormal type corresponding to the element whose value is not 0; The normal instruction of the external glue coating, the first abnormal instruction of the external glue coating, the second abnormal instruction of the external glue coating or the third abnormal instruction of the external glue coating constitutes the external monitoring and analysis data of the glue coating.

8. The glass gluing planning method based on artificial intelligence according to claim 3 or 7, characterized in that: The internal monitoring and analysis data of gluing and the external monitoring and analysis data of gluing in the quality monitoring and analysis set are traversed to determine the local supervision status of glass gluing corresponding to the supervision distance; If all the traversal results are normal instructions, then an overall normal instruction for gluing is generated and the subsequent glass gluing is controlled to continue with the preset parameters.

9. The artificial intelligence-based glass glue coating planning method according to claim 8, characterized in that: If there are abnormal instructions in the traversal results, an overall abnormal instruction for gluing is generated and the subsequent glass gluing is controlled to be suspended, and the abnormal data of the corresponding dimension is optimized and managed according to the abnormal instruction.

10. A glass gluing planning system based on artificial intelligence, using the glass gluing planning method based on artificial intelligence as claimed in any one of claims 1 to 9, characterized in that: include: The feature extraction processing and analysis module is used to pre-process the colloid image and extract the features to obtain the corresponding colloid features, perform feature processing on the colloid features and implement multi-dimensional colloid coating quality monitoring and analysis to obtain a quality monitoring and analysis set; the quality monitoring and analysis set is composed of internal monitoring and analysis data of the coating and external monitoring and analysis data of the coating; The multi-dimensional analysis result control module is used to determine the local supervision status of the glass glue coating corresponding to the supervision distance according to the monitoring and analysis data of different dimensions in the quality monitoring and analysis set, and to adaptively and dynamically control the subsequent glass glue coating according to the local supervision status of the glass glue coating.

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