Coating production line intelligent decision-making method and coating production line

Abstract

The invention provides a coating production line intelligent decision-making method and a coating production line, and the method comprises the steps: determining the service life period of a coating part on the coating production line; screening a target coating record according to the service life period, and training an intelligent decision model according to the target coating record; performing multi-objective optimization based on the intelligent decision-making model to obtain an intelligent decision-making result; and performing corresponding control on the coating production line based on the intelligent decision result. According to the intelligent decision-making method for the coating production line and the coating production line, the service life period of the coating part is determined by introducing the service time of the coating part, and the appropriate intelligent decision-making model is matched according to the service life period; according to the intelligent decision-making model, multi-objective optimization is carried out to obtain an intelligent decision-making result, corresponding control of the coating production line is carried out based on the intelligent decision-making result, and control of the coating production line is more intelligent and suitable.

Description

Technical Field

[0001] The present invention relates to the technical field of coating, and in particular to an intelligent decision-making method for a coating production line and a coating production line. Background Art

[0002] A coating line is an industrial production facility that is specifically designed to coat the surfaces of various products (such as automobiles, furniture, mechanical equipment, etc.). The purpose of a coating line is to evenly apply the coating to the surface of the product through a series of continuous process steps, and to ensure that the coating is dried and cured to achieve the requirements of protection, decoration or special functions (such as insulation, anti-corrosion, etc.). The design and configuration of a coating line will vary depending on factors such as product type, coating requirements, production volume and technical level. Modern coating lines are increasingly inclined to automation and intelligence to improve efficiency, reduce costs and ensure quality.

[0003] The invention patent with application number: CN202110150246.7 discloses a robot vision integration system for a coating production line, including a visual image acquisition module, a communication module, a fill light module, a visual image processing module, a visual image positioning module, a visual image measurement module, a visual tracking simulation module, a database, a control module and a spray route map output module; by setting the visual tracking simulation module, the database and the control module, a spray route simulation map can be formed according to the visual information of the actual workpiece, and the spray route simulation map is matched and searched with the existing spray route benchmark teaching map in the database to obtain an optimal target spray route map as the robot vision map in the spray production line, which can effectively reduce the impact of the robot's visual deviation on the spraying quality, thereby ensuring that the robot can obtain high spraying quality.

[0004] However, during the spraying operation of the coating production line, in addition to optimizing the spraying accuracy, multiple production line targets (such as spraying cost, spraying efficiency, etc.) need to be considered. The above-mentioned existing technology only considers the optimization of one production line production target. When faced with more diverse optimization needs, it is not smart enough and has low applicability.

[0005] In view of this, there is an urgent need for an intelligent decision-making method for a coating production line and a coating production line to at least solve the above-mentioned deficiencies. Summary of the invention

[0006] One of the purposes of the present invention is to provide an intelligent decision-making method and a coating production line for a coating production line, introduce the usage time of the coating parts, determine the service life period of the coating parts, and match a suitable intelligent decision-making model according to the service life period; perform multi-objective optimization according to the intelligent decision-making model to obtain intelligent decision results, and perform corresponding control of the coating production line based on the intelligent decision results, so that the control of the coating production line is more intelligent and appropriate.

[0007] The coating production line intelligent decision-making method provided by the embodiment of the present invention includes:

[0008] Determine the useful life period of the painted parts on the painting line;

[0009] Filter target painting records according to the service life period, and train intelligent decision-making models based on the target painting records;

[0010] Perform multi-objective optimization based on intelligent decision-making models to obtain intelligent decision-making results;

[0011] Carry out corresponding control of the coating production line based on the intelligent decision-making results.

[0012] Preferably, determining the service life period of the coating components on the coating production line includes:

[0013] Obtain equipment maintenance records for coating production lines;

[0014] Obtain the usage time of the painted parts based on the equipment maintenance records;

[0015] Get the consumable type of the painted part;

[0016] Determine the rules for the life cycle of the painted parts based on the type of consumables;

[0017] Determine the rules and usage duration based on the life cycle and determine the usage life period.

[0018] Preferably, the target painting records are screened according to the service life period, and the intelligent decision model is trained according to the target painting records, including:

[0019] Obtain pre-selected painting records; pre-selected painting records include: reference painting records obtained by big data and historical painting records on the painting production line;

[0020] Parse the pre-selected painting records to determine the reference service life period of the reference painted parts;

[0021] Determine the target coating record based on the service life period and the reference service life period;

[0022] Parse the target painting record to obtain a training data set; the training data set includes: a one-to-one corresponding training painting parameter set and a training painting quality parameter set;

[0023] Based on the preset CNN model, the intelligent decision-making model is trained according to the training data set.

[0024] Preferably, multi-objective optimization is performed based on the intelligent decision model to obtain intelligent decision results, including:

[0025] Based on the intelligent decision-making model, the optimization algorithm is used to perform multi-objective optimization to obtain intelligent decision-making results.

[0026] Preferably, based on the intelligent decision-making model, an optimization algorithm is used to perform multi-objective optimization to obtain intelligent decision-making results, including:

[0027] Get optimization target;

[0028] Based on the intelligent decision-making model, the Pareto optimal solution set is determined according to the optimization algorithm and optimization goal;

[0029] The Pareto optimal solution set is visualized as a Pareto frontier and presented to the production line managers, and intelligent decision-making results are obtained based on the target solution selected by the production line managers.

[0030] Preferably, the Pareto optimal solution set is visualized as a Pareto front and presented to the production line manager, including:

[0031] The optimization target is presented to the production line manager, and the optimization weight of the optimization target set by the production line manager is obtained;

[0032] According to the Pareto optimal solution set, determine the optimization quality parameter set corresponding to each Pareto optimal solution;

[0033] Obtain an initial quality parameter set;

[0034] Determine a quality improvement parameter set according to the optimized quality parameter set and the initial quality parameter set;

[0035] Determine the optimization value based on the quality improvement parameter set and optimization weight;

[0036] According to the corresponding relationship between the optimization value and the Pareto optimal solution, the optimization value is marked accordingly in the Pareto frontier.

[0037] Preferably, the corresponding control of the coating production line is performed based on the intelligent decision-making result, including:

[0038] Get the current painting parameter set;

[0039] Determine the control factors of the coating production line based on the current coating parameter set, intelligent decision-making results and the preset control factor library;

[0040] Control the coating production line for production based on the control factors.

[0041] Preferably, the corresponding control of the coating production line based on the intelligent decision-making result also includes:

[0042] Acquire a first coating process of a first coating target on a coating production line;

[0043] The switching moment for obtaining intelligent decision-making results;

[0044] Determine the second coating target according to the switching time and the first coating progress;

[0045] Performing image feature extraction on the second coating target to obtain image features;

[0046] determining a coating effect of a second coating target according to the image features;

[0047] If the painting effect is less than or equal to a preset painting effect threshold, the second painting target is readjusted.

[0048] Preferably, performing image feature extraction on the second coating target to obtain image features includes:

[0049] Determine the intervention process point according to the switching time and the first coating process;

[0050] According to the intervention process point, determine the intervention painting point of the second painting target;

[0051] Acquire a target section corresponding to the second coating target at the intervening coating point;

[0052] Obtaining a projection vector of the target section; the straight line where the projection vector is located is perpendicular to the target section, and the vector direction of the projection vector is from the intervention coating point to the outside of the second coating target;

[0053] Obtaining a shooting vector of a shooting device;

[0054] If for any projection vector, the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, and the corresponding shooting devices are triggered to shoot when the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, then the image features are obtained based on the corresponding shot images.

[0055] Preferably, determining the coating effect of the second coating target according to the image features includes:

[0056] Get the target attributes of the second painting target;

[0057] Obtain coating evaluation records based on target attributes;

[0058] Get the painting stage of the second painting target;

[0059] Determine the target evaluation record items in the coating evaluation record according to the coating stage;

[0060] According to the target evaluation record items, the evaluation model is trained;

[0061] Determine the evaluation prompts of the evaluation model according to the intervention coating points;

[0062] After the evaluation prompt is completed, the image features are input into the evaluation model to determine the painting effect of the second painting target.

[0063] The coating production line provided by the embodiment of the present invention applies the above-mentioned steps of intelligent decision-making of the coating production line.

[0064] The beneficial effects of the present invention are:

[0065] The present invention introduces the usage time of the painted parts, determines the service life period of the painted parts, and matches a suitable intelligent decision-making model according to the service life period; performs multi-objective optimization according to the intelligent decision-making model to obtain the intelligent decision result, and performs corresponding control of the coating production line based on the intelligent decision result, so that the control of the coating production line is more intelligent and appropriate.

[0066] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the present application documents.

[0067] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0069] Figure 1 Schematic diagram of the intelligent decision-making method for a coating production line in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0071] The embodiment of the present invention provides an intelligent decision-making method for a coating production line, such as Figure 1 As shown, including:

[0072] Step 1: Determine the service life period of the coating parts on the coating production line; the coating parts are: the parts that are used for coating operations on the coating production line, such as: brushes, spray gun nozzles, and filters on the coating line; the service life period is: the length of time the coating parts are put into use on the coating production line, obtained from the equipment maintenance records; the service life period is: the different periods of the coating parts in their service life, such as: initial, middle, and late periods;

[0073] Step 2: According to the service life period, the target painting records are screened, and the intelligent decision model is trained according to the target painting records; wherein, the target painting records are: the records of the historical painting operations of the reference painting parts that are at the same stage of the service life period, which are obtained through big data or local historical records; when training the intelligent decision model according to the target painting records, the target painting records are parsed to obtain the one-to-one corresponding training painting parameter set and training painting quality parameter set, the training painting parameter set is: various adjustable parameters involved in the painting process, such as: paint ratio, spraying pressure, drying temperature, etc.; the training painting quality parameter set is: indicators for evaluating the quality of painted products, such as: coating film thickness, coating film uniformity, etc.; introduce the CNN model, and train the intelligent decision model according to the training data set (the AI ​​model that determines the coating parameter set according to the required coating quality parameters);

[0074] Step 3: Perform multi-objective optimization based on the intelligent decision model to obtain intelligent decision results; wherein the intelligent decision results are: coating parameters used by coated parts that are suitable for the service life period. When performing multi-objective optimization, perform multi-objective optimization based on the multi-objective optimization algorithm and the intelligent decision model to obtain intelligent decision results;

[0075] Step 4: Control the coating production line accordingly based on the intelligent decision-making results. Among them, the corresponding control of the coating production line based on the intelligent decision-making results includes: obtaining the current coating parameter set; determining the control factor of the coating production line according to the current coating parameter set, the intelligent decision result and the preset control factor library; controlling the coating production line to produce based on the control factor; the control factor library includes the parameter differences and corresponding pre-selected control factors of multiple optimized coating parameter sets and the current coating parameter set, and the pre-selected control factor corresponding to the parameter difference that meets the situation is determined according to the current coating parameter set and the intelligent decision result and used as the control factor; the control factor is the control vector of the coating production line.

[0076] The working principle and beneficial effects of the above technical solution are:

[0077] The present invention introduces the usage time of the painted parts, determines the service life period of the painted parts, and matches a suitable intelligent decision-making model according to the service life period; performs multi-objective optimization according to the intelligent decision-making model to obtain the intelligent decision result, and performs corresponding control of the coating production line based on the intelligent decision result, so that the control of the coating production line is more intelligent and appropriate.

[0078] In one embodiment, according to the service life period, the target painting records are screened, and the intelligent decision model is trained according to the target painting records, including:

[0079] Obtain pre-selected painting records; the pre-selected painting records include: reference painting records obtained by big data and historical painting records on the painting production line; wherein, the reference painting records are: painting records on the painting production line obtained by big data; the historical painting records are: painting records on the local painting production line;

[0080] Parse the pre-selected painting record to determine the reference life cycle of the reference painting component; wherein the reference life cycle is: the life cycle stage of each reference painting component in the pre-selected painting record;

[0081] Determine a target painting record according to the service life period and the reference service life period; wherein the target painting record is a pre-selected painting record whose service life period of each painted component corresponds to the reference service life period of the reference painted component;

[0082] Parse the target painting record to obtain a training data set; the training data set includes: a one-to-one corresponding training painting parameter set and a training painting quality parameter set;

[0083] Based on the preset CNN model, the intelligent decision-making model is trained according to the training data set.

[0084] The working principle and beneficial effects of the above technical solution are:

[0085] The present invention obtains pre-selected painting records in two ways for subsequent intelligent decision-making model training, thereby improving the comprehensiveness of the training; matching the service life periods of painted parts and reference painted parts reduces the training errors caused by part differences and improves the suitability of the training.

[0086] In one embodiment, multi-objective optimization is performed based on the intelligent decision model to obtain intelligent decision results, including:

[0087] Based on the intelligent decision-making model, the optimization algorithm is used to perform multi-objective optimization to obtain intelligent decision-making results.

[0088] The working principle and beneficial effects of the above technical solution are:

[0089] The present invention is based on an intelligent decision-making model and utilizes an optimization algorithm to perform multi-objective optimization, thereby achieving a dynamic balance of multiple optimization objectives during the generation process, improving the balance of the optimization, and facilitating intelligent and reasonable decision-making for the coating production line.

[0090] In one embodiment, based on the intelligent decision model, an optimization algorithm is used to perform multi-objective optimization to obtain intelligent decision results, including:

[0091] Obtaining optimization targets; wherein the optimization targets are: the preset quality standards of various quality indicators, such as: the coating thickness, coating uniformity, etc., which are pre-entered by the staff;

[0092] Based on the intelligent decision-making model, the Pareto optimal solution set is determined according to the optimization algorithm and optimization objectives. The Pareto optimal solution set is: when no other solution can improve all objectives at the same time, any solution in a set of solutions cannot be improved by other solutions in at least one objective without deteriorating in other objectives.

[0093] The Pareto optimal solution set is visualized as a Pareto front and presented to the production line manager, and the intelligent decision-making results are obtained according to the target solution selected by the production line manager. The Pareto front is a graphical representation of the Pareto optimal solution in the target space.

[0094] The working principle and beneficial effects of the above technical solution are:

[0095] The present invention determines the Pareto optimal solution set according to the optimization target, and visualizes the complex Pareto optimal solution set as the Pareto frontier, so that production line managers can intuitively see the performance of different solutions, thereby improving the efficiency of selecting the target solution.

[0096] In one embodiment, the Pareto optimal solution set is visualized as a Pareto front and presented to the production line manager, including:

[0097] The optimization target is presented to the production line manager, and the optimization weight of the optimization target set by the production line manager is obtained; wherein the optimization weight is: a value representing the relative importance of the optimization target;

[0098] According to the Pareto optimal solution set, the optimized quality parameter set corresponding to each Pareto optimal solution is determined; wherein the optimized quality parameter set is: the specific values ​​of each quality index after optimization corresponding to the Pareto optimal solution;

[0099] Obtaining an initial quality parameter set; wherein the initial quality parameter set is: specific values ​​of each current quality indicator;

[0100] Determine a quality improvement parameter set according to the optimized quality parameter set and the initial quality parameter set; wherein the quality improvement parameter set includes the improvement degree of each quality indicator, and the improvement degree of the quality indicator is specifically: the improvement increment of the initial quality parameter divided by the initial quality parameter value;

[0101] Determine the optimization value according to the quality improvement parameter set and the optimization weight; wherein the optimization value is: the sum of the product of the quality improvement parameter corresponding to each quality indicator and the optimization weight;

[0102] According to the corresponding relationship between the optimization value and the Pareto optimal solution, the optimization value is marked accordingly in the Pareto frontier.

[0103] The working principle and beneficial effects of the above technical solution are:

[0104] When there are multiple optimization targets, the staff has different optimization preferences for each optimization target. The production line manager of the present invention can set the optimization weight according to the optimization preference. Determine the optimization quality parameter set corresponding to each Pareto optimal solution, compare the optimization quality parameter set with the initial quality parameter set, obtain the quality improvement parameter, and determine the optimization value corresponding to each Pareto optimal solution according to the quality improvement parameter and the set optimization weight; according to the corresponding relationship between the optimization value and the Pareto optimal solution, mark the optimization value in the Pareto frontier. The production line manager can intuitively view the optimization value of each Pareto optimal solution quantified according to the optimization weight set by himself, which is convenient for the selection of subsequent target solutions and more humane.

[0105] In one embodiment, the corresponding control of the coating production line based on the intelligent decision result also includes:

[0106] Obtaining a first coating process of a first coating target on a coating production line; wherein the first coating target is: a coating product on the coating production line; the first coating process is: a time node corresponding to each stage of different coating stages (such as: preparation, coating, drying);

[0107] Obtain the switching moment of the intelligent decision-making result; wherein the switching moment is: the moment of optimizing and adjusting the coating parameters;

[0108] Determine a second coating target according to the switching moment and the first coating process; wherein the second coating target is: the corresponding first coating target for optimizing and adjusting coating parameters during the coating process determined according to the switching moment and the first coating process;

[0109] Extracting image features of the second coating target to obtain image features; wherein the image features are extracted based on the captured image of the second coating target, and specifically include: color, texture, shape, etc.;

[0110] Determine the painting effect of the second painting target according to the image features; wherein, when determining the painting effect, obtain an evaluation model of the painting stage of the second painting target, and determine the painting effect according to the image features and the evaluation model;

[0111] If the coating effect is less than or equal to a preset coating effect threshold, the second coating target is readjusted, wherein the preset coating effect threshold is manually preset.

[0112] The working principle and beneficial effects of the above technical solution are:

[0113] After the production line products are painted, quality inspection is required. Due to problems such as control errors and optimization timing judgment errors, when the painting parameters change, the products corresponding to the spraying of the production line equipment are more likely to have quality problems. Therefore, the present invention obtains the first painting process of the first painting target, and in addition, obtains the switching time of the intelligent decision-making result, and determines the second painting target that is being painted when the painting parameter optimization occurs; extracts image features of the second painting target to obtain image features; introduces an evaluation model of the painting stage of the second painting target, and determines the painting effect according to the image features and the evaluation model; screens the second painting targets whose painting effects are less than or equal to the preset painting effect threshold and readjusts them, thereby improving the screening efficiency of problematic painted products.

[0114] In one embodiment, performing image feature extraction on the second coating target to obtain image features includes:

[0115] According to the switching time and the first coating process, determining the intervention process point; wherein the intervention process point is: the coating parameter optimization time;

[0116] According to the intervention process point, the intervention painting point of the second painting target is determined; wherein the intervention painting point is: the painting area point of the second painting target being painted corresponding to the painting parameter optimization moment;

[0117] Obtaining a target section corresponding to the second coating target at the intervening coating point; wherein the target section is tangent to the surface of the second coating target, and the tangent point is the intervening coating point;

[0118] Obtaining a projection vector of the target section; the straight line where the projection vector is located is perpendicular to the target section, and the vector direction of the projection vector is from the intervention coating point to the outside of the second coating target;

[0119] Obtaining a shooting vector of a shooting device; wherein the vector direction of the shooting vector is: vertically shooting the lens toward the outside of the lens;

[0120] If for any projection vector, the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, and the corresponding shooting devices are triggered to shoot when the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, then the image features are obtained based on the corresponding shot images.

[0121] The working principle and beneficial effects of the above technical solution are:

[0122] The present invention determines the intervening painting point of the second painting target, determines the target section with the intervening painting point as the tangent point and tangent to the surface of the second painting target; introduces the projection vector of the target section, and at the same time, introduces the shooting vector of the shooting device. If any projection vector satisfies that the vector angle between the shooting vector and the projection vector is greater than or equal to a preset angle threshold, it means that each intervening painting point that needs to be photographed has a suitable shooting situation; when the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, the corresponding shooting device triggers shooting, which means that image shooting is performed every time the shooting situation is suitable, and image features are acquired based on all captured images, thereby improving the suitability and comprehensiveness of image feature acquisition.

[0123] In one embodiment, determining the painting effect of the second painting target according to the image feature includes:

[0124] Obtaining a target attribute of the second coating target; wherein the target attribute is: attribute information of the second coating target, such as: coating object type;

[0125] According to the target attribute, a coating evaluation record is obtained; wherein the coating evaluation record is: evaluation record information of the coating staff evaluating the second coating target;

[0126] Obtaining the coating stage of the second coating target; wherein the coating stage is: different steps in the coating process, such as: primer coating, topcoat coating, drying, curing, etc.;

[0127] According to the painting stage, determining the target evaluation record item in the painting evaluation record; wherein the target evaluation record item is: the evaluation record information of the painting stage corresponding to the intervention process point;

[0128] According to the target evaluation record items, an evaluation model is trained; wherein the evaluation model is obtained by training the target evaluation record item model;

[0129] According to the intervention coating point, the evaluation prompt of the evaluation model is determined; wherein the evaluation prompt is: the position guidance information when the evaluation model is evaluating;

[0130] After the evaluation prompt is completed, the image features are input into the evaluation model to determine the painting effect of the second painting target.

[0131] The working principle and beneficial effects of the above technical solution are:

[0132] The present invention obtains a painting evaluation record of the second painting target evaluated by the painting staff according to the target attributes of the acquired second painting target; determines the target evaluation record item in the painting evaluation record according to the painting stage of the second painting target determined by the painting task process; trains an evaluation model suitable for the painting stage of the second painting target based on the target evaluation record item, which is more suitable; because the evaluation of the spraying effect is greatly affected by the shape of the second painting target, the evaluation prompt of the evaluation model is determined according to the intervening painting point, and the evaluation position of the evaluation model is guided to perform the painting evaluation. After the evaluation prompt is completed, the image feature is input into the evaluation model to determine the painting effect of the second painting target, and the painting effect evaluation is more refined.

[0133] The coating production line provided in the embodiment of the present invention is applied to the method described in the above embodiment.

[0134] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Intelligent decision-making method for coating production line, characterized in that: include: Determine the useful life period of the painted parts on the painting line; Filter target painting records according to the service life period, and train intelligent decision-making models based on the target painting records; Perform multi-objective optimization based on intelligent decision-making models to obtain intelligent decision-making results; Carry out corresponding control of the coating production line based on the intelligent decision-making results.

2. The coating production line intelligent decision-making method according to claim 1, characterized in that: Determine the useful life stage of the painted parts on the painting line, including: Obtain equipment maintenance records for coating production lines; Obtain the usage time of the painted parts based on the equipment maintenance records; Get the consumable type of the painted part; Determine the rules for the life cycle of the painted parts based on the type of consumables; Determine the rules and usage duration based on the life cycle and determine the usage life period.

3. The coating production line intelligent decision-making method according to claim 1, characterized in that: According to the service life period, the target painting records are screened and the intelligent decision model is trained based on the target painting records, including: Obtain pre-selected painting records; pre-selected painting records include: reference painting records obtained by big data and historical painting records on the painting production line; Parse the pre-selected painting records to determine the reference service life period of the reference painted parts; Determine the target coating record based on the service life period and the reference service life period; Parse the target painting record to obtain a training data set; the training data set includes: a one-to-one corresponding training painting parameter set and a training painting quality parameter set; Based on the preset CNN model, the intelligent decision-making model is trained according to the training data set.

4. The coating production line intelligent decision-making method according to claim 1, characterized in that: Based on the intelligent decision-making model, multi-objective optimization is performed to obtain intelligent decision-making results, including: Based on the intelligent decision-making model, the optimization algorithm is used to perform multi-objective optimization to obtain intelligent decision-making results.

5. The coating production line intelligent decision-making method according to claim 4, characterized in that: Based on the intelligent decision-making model, the optimization algorithm is used to perform multi-objective optimization to obtain intelligent decision-making results, including: Get optimization target; Based on the intelligent decision-making model, the Pareto optimal solution set is determined according to the optimization algorithm and optimization goal; The Pareto optimal solution set is visualized as a Pareto frontier and presented to the production line managers, and intelligent decision-making results are obtained based on the target solution selected by the production line managers.

6. The coating production line intelligent decision-making method according to claim 5, characterized in that: Visualize the set of Pareto optimal solutions as a Pareto front and present it to production line managers, including: The optimization target is presented to the production line manager, and the optimization weight of the optimization target set by the production line manager is obtained; According to the Pareto optimal solution set, determine the optimization quality parameter set corresponding to each Pareto optimal solution; Obtain an initial quality parameter set; Determine a quality improvement parameter set according to the optimized quality parameter set and the initial quality parameter set; Determine the optimization value based on the quality improvement parameter set and optimization weight; According to the corresponding relationship between the optimization value and the Pareto optimal solution, the optimization value is marked accordingly in the Pareto frontier.

7. The coating production line intelligent decision-making method according to claim 1, characterized in that: Based on the intelligent decision-making results, the coating production line is controlled accordingly, including: Get the current painting parameter set; Determine the control factors of the coating production line based on the current coating parameter set, intelligent decision-making results and the preset control factor library; Control the coating production line for production based on the control factors.

8. The coating production line intelligent decision-making method according to claim 1, characterized in that: Based on the intelligent decision-making results, the corresponding control of the coating production line also includes: Acquire a first coating process of a first coating target on a coating production line; The switching moment for obtaining intelligent decision-making results; Determine the second coating target according to the switching time and the first coating progress; Performing image feature extraction on the second coating target to obtain image features; determining a coating effect of a second coating target according to the image features; If the painting effect is less than or equal to a preset painting effect threshold, the second painting target is readjusted.

9. The coating production line intelligent decision-making method according to claim 8, characterized in that: Perform image feature extraction on the second coating target to obtain image features, including: Determine the intervention process point according to the switching time and the first coating process; According to the intervention process point, determine the intervention painting point of the second painting target; Acquire a target section corresponding to the second coating target at the intervening coating point; Obtaining a projection vector of the target section; the straight line where the projection vector is located is perpendicular to the target section, and the vector direction of the projection vector is from the intervention coating point to the outside of the second coating target; Obtaining a shooting vector of a shooting device; If for any projection vector, the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, and the corresponding shooting devices are triggered to shoot when the vector angle between the shooting vector and the projection vector is greater than or equal to the preset angle threshold, then the image features are obtained based on the corresponding shot images.

10. The coating production line is characterized by: The method according to claims 1 to 9 is applied.

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