Method and device for adaptively controlling process parameters of automobile coating

Abstract

The present invention relates to the technical field of coating process, and in particular to a method and device for adaptively controlling automobile coating process parameters, comprising: collecting a set of real-time variables of a process environment, a set of real-time variables of a spraying equipment state, and a set of real-time variables of coating characteristics in a coating workshop; calculating the degree of deviation between the real-time variables of the process environment, the real-time variables of the spraying equipment state, and the real-time variables of coating characteristics and a pre-entered set of standard variables of the process environment, the standard variables of the spraying equipment state, and the standard variables of coating characteristics, respectively, to obtain a multi-modal vector of real-time deviation of the coating process; inputting the multi-modal vector of real-time deviation of the coating process into a pre-constructed coating process deviation analysis model to obtain a quantitative value of coating process deviation evaluation; and reducing energy consumption and material waste by precisely controlling coating process parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of coating technology, and in particular to a method and device for adaptively controlling parameters of automobile coating technology. Background Art

[0002] With the rapid development of the automobile industry and the increasing demand of consumers for automobile appearance quality, automobile painting technology has become an important link in determining the final quality of automobiles; however, the painting process is affected by many factors, including environmental conditions in the paint shop, equipment operating parameters, and characteristic parameters of the paint itself; the complex interaction between these factors often leads to unstable painting effects and inconsistent product quality.

[0003] Traditional automotive coating process parameter control mostly relies on experience and fixed parameter settings, and lacks the ability to respond to environmental changes and equipment status changes in real time. This static control method is often difficult to cope with complex real-time changing factors, such as workshop environment fluctuations, equipment status changes, and changes in coating properties. These factors may lead to unstable coating quality, increased energy consumption, and material waste.

[0004] Therefore, in order to solve the above problems, an adaptive control method for automobile painting process parameters is urgently needed. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and device for adaptively controlling automobile painting process parameters, which can reduce energy consumption and material waste by accurately controlling painting process parameters.

[0006] In a first aspect, the present invention provides a method for adaptively controlling automobile coating process parameters, the method comprising:

[0007] Collect the real-time variable set of the process environment in the paint shop, the real-time variable set of the spraying equipment status, and the real-time variable set of the paint's own characteristics;

[0008] Calculate the deviations between the process environment real-time variable set, the spray equipment state real-time variable set and the paint's own characteristic real-time variable set and the pre-entered process environment standard variable set, the spray equipment state standard variable set and the paint's own characteristic standard variable set, respectively, to obtain a multi-modal vector of the coating process real-time deviation;

[0009] Input the multi-modal vector of the real-time deviation of the coating process into the pre-built coating process deviation analysis model to obtain the quantitative value of the coating process deviation evaluation;

[0010] In response to the coating process deviation evaluation quantization value being higher than a preset coating process deviation evaluation threshold, converting a process environment real-time variable set, a spraying equipment state real-time variable set, and a coating material own characteristic real-time variable set into a coating process real-time characterization data set;

[0011] Input the coating process real-time characterization data set into the pre-built coating process parameter optimization model to obtain the coating process parameter adaptive control instruction set;

[0012] The painting operation is performed according to the adaptive control instruction set of the painting process parameters.

[0013] On the other hand, the present application also provides an adaptive control device for automobile coating process parameters, the device comprising:

[0014] The data acquisition module is used to collect the real-time variable set of the process environment in the paint shop, the real-time variable set of the spraying equipment status, and the real-time variable set of the paint's own characteristics;

[0015] The deviation calculation module is used to calculate the deviation between the process environment real-time variable set, the spray equipment state real-time variable set and the paint's own characteristic real-time variable set and the pre-entered process environment standard variable set, the spray equipment state standard variable set and the paint's own characteristic standard variable set, and obtain the coating process real-time deviation multi-modal vector;

[0016] The deviation analysis module is used to input the multi-modal vector of the real-time deviation of the coating process into the pre-built coating process deviation analysis model, and output the coating process deviation evaluation quantitative value;

[0017] A decision control module is used to determine whether the coating process deviation evaluation quantification value is higher than a preset coating process deviation evaluation threshold; when the coating process deviation evaluation quantification value is higher than the preset coating process deviation evaluation threshold, the parameter optimization process is triggered, and the process environment real-time variable set, the spray equipment status real-time variable set and the coating's own characteristic real-time variable set are converted into a coating process real-time characterization data set;

[0018] Parameter optimization module, which is used to input the real-time characterization data set of the coating process into the built-in coating process parameter optimization model and output the coating process parameter adaptive control instruction set;

[0019] The painting execution module adaptively controls the instruction set according to the painting process parameters and executes the corresponding painting operations.

[0020] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: by real-time monitoring of the process environment, spray equipment status and paint characteristics in the paint shop, it is possible to quickly capture the real-time changes of various factors, realize real-time response to environmental fluctuations, equipment status changes and changes in paint characteristics, and improve the adaptability and flexibility of the painting process; by calculating the deviation between the real-time variable set and the standard variable set, a multi-modal vector of the real-time deviation of the painting process is formed, and then the painting process deviation analysis model is used to obtain the quantitative value of the painting process deviation evaluation; this process realizes the accurate identification and quantitative evaluation of the painting process deviation, provides a scientific basis for the subsequent parameter optimization, and helps to timely discover and correct problems in the process; when the quantitative value of the painting process deviation evaluation is higher than the preset threshold, the real-time variable is set. The set is converted into a real-time characterization data set for the coating process, and is input into the coating process parameter optimization model to generate an adaptive control instruction set for the coating process parameters; this intelligent optimization process can automatically adjust the coating process parameters according to the real-time process status to ensure that the coating process is always in the optimal state, effectively improving the coating quality and efficiency; this method can continuously iterate and optimize the control instructions through continuous monitoring and feedback of the coating effect, realize dynamic adjustment and continuous improvement of the coating process, and enhance the stability and consistency of the coating process; by precisely controlling the coating process parameters, it effectively reduces the problems of unstable coating quality and increased rework rate caused by environmental fluctuations, equipment failures or changes in coating properties, thereby reducing energy consumption and material waste, which is in line with the requirements of the modern automotive industry for energy saving, consumption reduction and green environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1It is a flow chart of a method for adaptively controlling automobile coating process parameters provided by one embodiment of the present invention;

[0025] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0026] Figure 3 It is a structural diagram of an automobile painting process parameter adaptive control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Please refer to Figure 1 The embodiment of the present invention provides a method for adaptively controlling automobile coating process parameters, comprising the following steps:

[0029] Step S1, collecting a real-time variable set of the process environment in the paint shop, a real-time variable set of the spraying equipment status, and a real-time variable set of the paint's own characteristics;

[0030] Step S2, calculating the deviation between the process environment real-time variable set, the spraying equipment state real-time variable set and the coating's own characteristic real-time variable set and the pre-entered process environment standard variable set, the spraying equipment state standard variable set and the coating's own characteristic standard variable set, respectively, to obtain a coating process real-time deviation multimodal vector;

[0031] Step S3, inputting the multi-modal vector of the real-time deviation of the coating process into a pre-built coating process deviation analysis model to obtain a coating process deviation evaluation quantitative value;

[0032] Step S4, in response to the coating process deviation evaluation quantization value being higher than a preset coating process deviation evaluation threshold, converting the process environment real-time variable set, the spraying equipment state real-time variable set, and the coating itself characteristic real-time variable set into a coating process real-time characterization data set;

[0033] Step S5, inputting the coating process real-time characterization data set into a pre-built coating process parameter optimization model to obtain a coating process parameter adaptive control instruction set;

[0034] Step S6, performing painting operation according to the painting process parameter adaptive control instruction set.

[0035] In this embodiment, by real-time monitoring of the process environment, spray equipment status and paint characteristics in the paint shop, the real-time changes of various factors can be quickly captured, and real-time response to environmental fluctuations, equipment status changes and paint characteristics changes can be achieved, thereby improving the adaptability and flexibility of the painting process; by calculating the deviation between the real-time variable set and the standard variable set, a multi-modal vector of the real-time deviation of the painting process is formed, and then the painting process deviation analysis model is used to obtain the quantitative value of the painting process deviation evaluation; this process realizes the accurate identification and quantitative evaluation of the painting process deviation, provides a scientific basis for the subsequent parameter optimization, and helps to timely discover and correct problems in the process; when the quantitative value of the painting process deviation evaluation is higher than the preset threshold, the real-time variable set is converted into a standard deviation vector. The coating process is characterized in real time and input into the coating process parameter optimization model to generate a set of adaptive control instructions for coating process parameters. This intelligent optimization process can automatically adjust the coating process parameters according to the real-time process status to ensure that the coating process is always in the optimal state, effectively improving the coating quality and efficiency. Through continuous monitoring and feedback of the coating effect, this method can continuously iterate and optimize the control instructions, realize dynamic adjustment and continuous improvement of the coating process, and enhance the stability and consistency of the coating process. Through precise control of the coating process parameters, the coating quality instability and increased rework rate caused by environmental fluctuations, equipment failures or changes in coating properties are effectively reduced, thereby reducing energy consumption and material waste, which is in line with the requirements of the modern automotive industry for energy saving, consumption reduction and green environmental protection.

[0036] Described below Figure 1 How the various steps are performed.

[0037] For step S1:

[0038] In the automotive coating process, it is crucial to ensure the stability of the coating effect and the consistency of product quality. To achieve this goal, it is necessary to use special sensors, detection instruments and systems to monitor and collect data on key process parameters in the coating workshop in real time and accurately. How to obtain the relevant real-time variables of the three core elements that affect the coating process (process environment, spray equipment status and coating characteristics) are as follows:

[0039] The process environment real-time variable set mainly reflects the current environmental conditions of the paint shop, which have a direct impact on the painting effect and coating performance; specifically, it includes:

[0040] Ambient temperature: Use temperature sensors to continuously monitor the air temperature in the paint shop to ensure that it is within the appropriate range recommended by the paint manufacturer to maintain the proper viscosity and drying speed of the paint and prevent coating defects such as sagging and orange peel;

[0041] Ambient humidity: Use humidity sensors to monitor relative air humidity. Too high or too low humidity may cause the paint to dry too quickly or too slowly, affecting the leveling, adhesion and final gloss of the coating. Maintaining appropriate humidity helps ensure optimal performance of the paint.

[0042] Air dust particle concentration: Use an air particle detector to monitor the number and size of dust particles in the air, because too many dust particles may adhere to the surface of the car body during the painting process, resulting in a rough coating surface and increased defects; regular cleaning and strict control of workshop air cleanliness are crucial to improving painting quality;

[0043] Wind speed: Use an anemometer to monitor the air flow in the workshop, especially the wind speed near the spraying area; reasonable wind speed helps the paint to be evenly distributed and dry quickly, but too high wind speed may cause paint to splash, waste and pollute the surrounding environment;

[0044] Light intensity: For certain coating processes (such as light-curing coatings), light intensity sensors are used to measure the light intensity in the work area to ensure that it reaches the level required to initiate the coating curing reaction; appropriate light intensity helps ensure fast curing and good performance of the coating;

[0045] Ambient air pressure: Although paint shops usually maintain a constant air pressure to ensure the normal operation of equipment, changes in air pressure may affect the atomization effect of the paint and the coating quality; the air pressure data of the workshop is continuously monitored and recorded through the air pressure sensor so that adjustments can be made when necessary.

[0046] The real-time variable set of spray equipment status focuses on the operating status and performance parameters of the spray equipment, which directly affect the coating efficiency and coating quality; specifically, they include:

[0047] Motor temperature: monitor the temperature of the spray gun drive motor or other key equipment motors to prevent overheating that may cause equipment damage or performance degradation; read and record the motor temperature in real time through a temperature sensor to ensure that the equipment operates within a safe temperature range;

[0048] Motor speed: Use a speed sensor to measure the rotation speed of the motor. Changes in speed may reflect the workload or fault conditions of the equipment. Maintaining a stable motor speed helps maintain the stable working state of the spray equipment and ensure the coating effect.

[0049] Motor current and motor voltage: Monitor the current and voltage of the motor when it is running. These data help to determine the health of the equipment's electrical system and detect electrical faults or inefficiencies in a timely manner. The motor current and voltage values ​​are continuously recorded through current transformers and voltmeters.

[0050] Fault indication: Monitor the equipment for abnormal conditions or fault warning signals, such as overload, blocking fault, electrical fault, etc., through the equipment's own fault detection system or by installing additional sensors; timely acquisition and response to fault information can reduce unplanned downtime and maintenance costs;

[0051] Pumping efficiency: For the paint delivery system, the efficiency of pumping paint is monitored through flow meters, pressure gauges and other equipment, including parameters such as paint flow and pressure; maintaining a stable pumping efficiency helps ensure the continuity of paint supply and the consistency of coating effects.

[0052] The real-time variable set of coating characteristics focuses on the real-time physical and chemical characteristics of the coating during the coating process. These characteristics determine whether the coating can perform optimally under specific conditions; specifically, they include:

[0053] Paint viscosity: Use an online viscometer or take samples regularly to measure the viscosity of the paint to ensure that it meets the requirements of the coating process; too high or too low paint viscosity may cause coating defects such as flow, pinholes, orange peel, etc.

[0054] Solid content: monitor the solid content of the coating to ensure that it meets product specifications and coating process requirements; solid content directly affects the coating's thickness, hardness, wear resistance and other properties;

[0055] pH value: Use a pH meter to regularly test the pH of the coating to ensure that it is within the appropriate range; pH value has an important impact on the stability, curing speed, and compatibility with other components of the coating;

[0056] Pigment volume concentration: measures the ratio of pigment to resin in the coating. The pigment volume concentration directly affects the coating's hiding power, weather resistance, mechanical properties, etc.

[0057] Flash time: For coatings that require pretreatment, record the standing time from opening the can to before use to ensure that the coating is fully degassed and the temperature is uniform to obtain the best coating effect;

[0058] Paint temperature: Use a temperature sensor to monitor the paint temperature in the paint tank or paint pipeline to ensure it is within the recommended painting temperature range; paint temperature directly affects its viscosity, atomization effect and coating quality.

[0059] In this step, various sensors and detection equipment are used to collect data on these variables in real time, and the accuracy and reliability of the data are ensured; these data not only provide basic data support for deviation calculation, deviation analysis, parameter optimization, etc. in subsequent steps, but also serve as the key basis for realizing adaptive control of coating process parameters; only by accurately grasping the data of these real-time variables can the coating process parameters be effectively adjusted and optimized in real time, thereby ensuring coating quality and stability.

[0060] For step S2:

[0061] In the method for adaptively controlling automobile coating process parameters, the core task of step S2 is to quantitatively evaluate the degree of deviation between various real-time variables in the current coating process and the preset standard values, so as to carry out effective deviation analysis and process parameter optimization later; the pre-entered standard values ​​refer to the target values ​​that various parameters should reach under ideal process conditions; these standard values ​​are determined according to the coating process requirements, the equipment manufacturer's recommendations and the technical data provided by the coating supplier, to determine the process environment standard variable set, the spray equipment status standard variable set and the coating's own characteristic standard variable set; these standard values ​​represent the ideal working conditions of the coating process and are the basis for measuring the deviation of real-time variables.

[0062] For each real-time variable in the process environment real-time variable set, the spraying equipment status real-time variable set and the coating characteristic real-time variable set, the deviation between it and the corresponding standard value is calculated respectively. The deviation calculation formula is as follows:

[0063]

[0064] Among them, ΔR represents the deviation of the variable, X t Indicates the real-time variable value, X s Indicates standard value.

[0065] The deviation of each real-time variable is organized according to its category (process environment, spray equipment status, and paint characteristics) to form three groups of deviation vectors. These three groups of vectors are then combined into a multi-modal vector of real-time deviation of the coating process. The multi-modal vector of real-time deviation of the coating process contains comprehensive information on the deviation between all key variables and standard values ​​in the coating process. For example, assuming that the multi-modal vector of real-time deviation of the coating process is denoted as D, its form is as follows:

[0066] D=[D e ,D d ,D p ];

[0067] D e =[ΔT e ,ΔH,ΔC,ΔV,ΔL,ΔP];

[0068] D d =[ΔT d ,Δω,ΔI,ΔU,ΔF,Δη];

[0069] D p =[Δη c ,ΔS,ΔpH,ΔPVC,Δt f ,ΔT p ];

[0070] Where, ΔT e ,ΔH,ΔC,ΔV,ΔL,ΔP represent the deviation of ambient temperature, ambient humidity, air dust particle concentration, wind speed, light intensity and ambient air pressure respectively;

[0071] ΔT d ,Δω,ΔI,ΔU,ΔF,Δη d Respectively represent the deviation of motor temperature, motor speed, motor current, motor voltage, fault indication and pumping efficiency;

[0072] Δη c ,ΔS,ΔpH,ΔPVC,Δt f ,ΔT p They represent the deviation of coating viscosity, solid content, pH value, pigment volume concentration, flash time and coating temperature respectively.

[0073] This step covers the key variables in the coating process, including the process environment, the state of the spraying equipment and the characteristics of the coating itself, ensuring comprehensive monitoring and evaluation of the multi-dimensional factors that affect the coating effect and product quality; this comprehensiveness helps to reveal various problems that may arise in the coating process and provide comprehensive reference information for subsequent process optimization; by calculating the deviation between the real-time variables and the standard values, the original qualitative or empirical process status evaluation is converted into quantitative data, which improves the objectivity and accuracy of the evaluation; this quantitative method facilitates standardized and systematic comparison and analysis of the coating process status, which is conducive to making decisions based on data support; A multimodal vector of real-time deviation of the coating process is constructed, which integrates the deviation information of the three aspects of environment, equipment and coating into a unified vector, realizing the fusion of multi-source information; this fusion helps to reveal the interaction and influence of different factors, and provides multi-perspective and multi-level support for the comprehensive understanding and optimization of the coating process; the calculated multimodal vector of real-time deviation of the coating process provides accurate and real-time input data for subsequent deviation analysis, parameter optimization and coating operations; based on these data, process deviations can be effectively identified, process status can be evaluated, optimization strategies can be formulated, and adaptive regulation can be performed, thereby improving coating quality and reducing energy consumption and material waste.

[0074] For step S3:

[0075] In the method for adaptively controlling the parameters of automobile coating process, the main purpose of step S3 is to conduct an in-depth analysis of the multi-modal vector of the real-time deviation of the coating process calculated in step S2, quantitatively evaluate the overall deviation of the coating process, and provide a basis for subsequent process parameter optimization; the specific implementation process is as follows:

[0076] Step S31, constructing a coating process deviation analysis model: before starting, a coating process deviation analysis model needs to be constructed in advance; the model is usually established based on methods such as machine learning, statistical analysis or expert knowledge system, and can comprehensively consider the impact of multiple factors such as environment, equipment and coating characteristics on the coating effect, as well as the interaction between these factors; the construction of the model is usually based on historical coating data, experimental data, theoretical research and industry experience, and is trained, verified and optimized to ensure that it has good generalization ability and prediction accuracy;

[0077] Step S32, input the multimodal vector of the real-time deviation of the coating process: the multimodal vector of the real-time deviation of the coating process calculated in step S2 is used as input and sent to the pre-built coating process deviation analysis model; the multimodal vector of the real-time deviation of the coating process contains comprehensive information on the deviations between all key variables and standard values ​​in the coating process, and is the basis for the coating process deviation analysis model to perform deviation analysis;

[0078] Step S33, model calculation and output: The coating process deviation analysis model calculates the input coating process real-time deviation multimodal vector, and based on its internal algorithm logic and parameter weights, comprehensively evaluates the influence of various deviations on the coating effect and the interaction between them; the coating process deviation analysis model outputs a coating process deviation evaluation quantitative value, which represents the overall deviation of the current coating process; the coating process deviation evaluation quantitative value reflects the degree of deviation of the coating process from the ideal state. The lower the value, the smaller the deviation of the coating process from the standard value, and the closer the coating effect is to the ideal state; for example, the calculation formula of the coating process deviation analysis model is:

[0079]

[0080] Where Q represents the quantitative value of coating process deviation evaluation, n, m and l represent the number of variables in the process environment real-time variable set, the spraying equipment status real-time variable set and the coating's own characteristic real-time variable set respectively; T e,i , T d,j and T p,k They represent the deviation of the i-th process environment real-time variable, the j-th spraying equipment status real-time variable and the k-th coating characteristic real-time variable respectively; β e,i , β d,j and β p,k are the weight coefficients corresponding to the deviation of the real-time variable of the i-th process environment, the deviation of the real-time variable of the j-th spraying equipment status, and the real-time variable of the k-th coating characteristic; ω e ,ω d and ω p They are the weight coefficients of the process environment real-time variable set, the spray equipment status real-time variable set and the coating's own characteristic real-time variable set;

[0081] Through the above process, the multi-modal vector of the real-time deviation of the coating process is processed by the coating process deviation analysis model to obtain the quantitative value of the coating process deviation evaluation; the quantitative value of the coating process deviation evaluation provides a comprehensive evaluation result of the coating process deviation for the subsequent steps, and provides a basis for judging whether process parameter optimization is needed and how to optimize it, which helps to achieve adaptive control of coating process parameters and improve coating quality and production efficiency.

[0082] For step S4:

[0083] Step S4 involves judging the quantitative value of the coating process deviation evaluation, and deciding whether to convert the coating process real-time characterization data set according to the judgment result; the implementation of step S4 is crucial to ensuring the coating quality and improving the stability of the coating process; specifically, the implementation process of step S4 is as follows:

[0084] Step S41, obtaining the coating process deviation evaluation quantitative value: in step S3, the coating process deviation evaluation quantitative value has been calculated by the coating process deviation analysis model; the coating process deviation evaluation quantitative value is a quantitative description of the current coating process deviation, which is used to judge the stability and consistency of the current coating process;

[0085] Step S42, comparison with a preset coating process deviation evaluation threshold: comparing the obtained coating process deviation evaluation quantified value with a preset coating process deviation evaluation threshold; the preset coating process deviation evaluation threshold is set according to the actual requirements of the coating process and the product quality requirements, and it represents the allowable range of the coating process deviation; if the coating process deviation evaluation quantified value is lower than the preset coating process deviation evaluation threshold, it means that the current coating process deviation is within the allowable range, and the coating operation can continue; if the coating process deviation evaluation quantified value is higher than the preset coating process deviation evaluation threshold, it means that the current coating process deviation exceeds the allowable range and needs to be adjusted and optimized;

[0086] Step S43, real-time variable set integration: If the coating process deviation evaluation quantization value is higher than the preset coating process deviation evaluation threshold, it means that the current coating process deviation exceeds the allowable range; at this time, the three real-time variable sets collected in step S1 need to be integrated; the converted coating process real-time characterization data set contains key information that can reflect the coating process status and coating quality; specifically, it includes a process environment real-time variable set (ambient temperature, humidity, air dust particle concentration, wind speed, light intensity, ambient air pressure), a spraying equipment status real-time variable set (motor temperature, speed, current, voltage, fault indication, pumping efficiency) and a coating property real-time variable set (coating viscosity, solid content, pH value, pigment volume concentration, flash time, coating temperature);

[0087] Step S44, data standardization and normalization: Since different types of real-time variables may have different dimensions and value ranges, in order to make them comparable and consistent in subsequent models, these variables are usually standardized or normalized; this step may include but is not limited to: converting each variable into a standard normal distribution form with a mean of 0 and a standard deviation of 1, such as Z-score standardization; mapping the value range of each variable to between [0, 1] or [-1, 1], such as minimum-maximum normalization;

[0088] Step S45, data set construction: After the above processing, each real-time observation point (usually the measurement value at a certain moment or within a certain period) will be converted into a data record containing the integrated, standardized, and feature-engineered values ​​of each variable; all such records that meet the conditions constitute the real-time characterization data set of the coating process.

[0089] In this step, by comparing the quantitative value of the coating process deviation evaluation with the preset threshold in real time, the deviation of the process from the ideal state can be quickly identified; once it is found that the deviation exceeds the allowable range, the subsequent data conversion process is immediately triggered to ensure a rapid response to process abnormalities, which is conducive to timely adjustment of coating parameters and avoid further expansion of quality problems; the integrated real-time variable set covers the three core elements of the coating process: process environment, spray equipment status and coating characteristics; such a design ensures comprehensive monitoring of multi-dimensional factors affecting coating quality, which helps to accurately identify the root cause of the problem; at the same time, these variables are standardized and normalized to ensure the comparability of data of different natures, so that the model can accurately extract key information from the data; the constructed coating process real-time characterization data set provides rich and structured data input for the subsequent parameter optimization model; based on these data, the model can generate coating process parameter adaptive control instructions in a data-driven manner, avoiding the problems of inaccurate or delayed control that may result from relying solely on experience or fixed parameter settings.

[0090] For step S5:

[0091] Step S5 is the core link in the method for adaptively controlling the parameters of automobile coating process, which is responsible for converting the real-time characterization data set of the coating process into specific coating process parameter control instructions to achieve precise control of the coating process; the detailed process of this step is as follows:

[0092] Step S51: using the coating process real-time characterization data set obtained in step S4 as input, the data set has integrated the process environment real-time variable set, the spray equipment status real-time variable set and the coating itself characteristic real-time variable set, and has been standardized and normalized to ensure the consistency and comparability of the data; these data fully reflect the actual state of the current coating process, including key factors such as environmental conditions, equipment performance and coating characteristics;

[0093] Step S52: Use a pre-built coating process parameter optimization model to analyze the input coating process real-time characterization data set; the model usually uses a machine learning algorithm (such as a neural network, a support vector machine, a decision tree, etc.) or a simulation optimization method based on a physical model; the model has been trained or calibrated and has the ability to predict key indicators such as coating quality, energy consumption, and material utilization rate based on real-time data, and can derive the optimal process parameter combination based on this;

[0094] Step S53: The result of the coating process parameter optimization model operation is to generate a coating process parameter adaptive control instruction set; the coating process parameter adaptive control instruction set includes specific adjustment suggestions for multiple key parameters that directly affect the coating effect, such as:

[0095] Spraying pressure adjustment: The coating process parameter optimization model calculates the ideal spraying pressure value based on real-time data to ensure that the paint adheres evenly to the surface of the vehicle body, avoiding defects such as uneven coating thickness, sagging or orange peel caused by overspray or underspray;

[0096] Spray gun distance adjustment: The coating process parameter optimization model recommends the appropriate distance between the spray gun and the body surface to ensure the best balance between atomization effect, paint utilization and coating appearance quality;

[0097] Spray gun angle adjustment: The coating process parameter optimization model guides the optimal tilt angle of the spray gun relative to the body surface to adapt to changes in body shape, ensure uniform coating coverage, and reduce edge accumulation or missed coating;

[0098] Atomized particle size control: Based on the coating characteristics and environmental conditions, the coating process parameter optimization model determines the atomized particle size range that is most conducive to forming a uniform coating film to enhance the coating smoothness and glossiness while reducing the risk of overspray;

[0099] Paint flow control: The coating process parameter optimization model calculates the paint delivery rate that matches the current process conditions to maintain stable coating efficiency and avoid waste caused by excessive flow or coating defects caused by too small flow;

[0100] Step S54, the painting operator or the automated control system adjusts the parameters of the painting equipment according to the adaptive control instruction set of the painting process parameters; in actual applications, the control instructions may be automatically sent to the relevant equipment through the digital control system to achieve closed-loop control; in addition, the system should also have the ability to monitor the painting effect in real time, collect product quality data after painting, and feed it back to the model for iterative learning and optimization, so as to continuously improve the control accuracy and adaptability.

[0101] In this step, by integrating real-time environment, equipment and coating data and combining with advanced optimization models, the optimal coating parameters for current process conditions can be accurately calculated; this adaptive control mechanism can effectively respond to environmental fluctuations, equipment status changes and coating property differences, ensure that the coating process is always in the best operating state, and improve the consistency of coating quality; compared with the traditional method of relying on experience or fixed parameter settings, step S5 can receive and process real-time data in real time, quickly generate control instructions, and realize instant adjustment of coating process parameters; this dynamic response capability helps to reduce unnecessary waiting time and improve production efficiency, and also helps to timely discover and correct potential problems, avoid defective products, and reduce rework rate; through fine control of key parameters such as spraying pressure, spray gun distance, spray gun angle, atomization particle size and paint flow, step S5 helps to reduce overspray and underspray of paint, thereby reducing paint waste and saving raw material costs; at the same time, the optimized process parameters can also reduce energy consumption, such as reducing compressed air consumption through appropriate pressure and flow control, which meets the requirements of modern automobile industry for energy conservation and emission reduction.

[0102] For step S6:

[0103] Step S6 is the execution stage of the method for adaptively controlling the process parameters of automobile coating. Its main task is to accurately adjust the coating operation according to the adaptively controlling instruction set of the coating process parameters generated in the previous step S5, so as to realize real-time control and optimization of the coating process. The specific operation process is as follows:

[0104] Step S61, parsing the coating process parameter adaptive control instruction set outputted in step S5, and clarifying the target setting value of each parameter; the coating process parameter adaptive control instruction set includes specific adjustment suggestions for key parameters such as spraying pressure, spray gun distance, spray gun angle, atomized particle size and paint flow rate;

[0105] Step S62, according to the coating process parameter adaptive control instruction set, the control system starts to adjust the parameters of the coating equipment; for the spraying pressure, adjust the pressure controller of the paint supply system or the air compressor to set it to the pressure value recommended by the instruction; for the spray gun distance, adjust the distance between the spray gun and the body surface to the specified range by moving the mechanical arm or adjusting the spray gun bracket; for the spray gun angle, use the spray gun angle adjustment mechanism or robot programming to set the inclination angle between the spray gun and the body surface; for the atomized particle size, replace the atomizer accessories or adjust the atomizing air pressure to make the paint atomization reach the particle size range recommended by the instruction; for the paint flow rate, adjust the speed of the paint delivery pump or the valve opening to achieve the paint delivery rate required by the instruction;

[0106] Step S63, after executing the painting process, according to the set time interval, collect the process environment real-time variable set, the spray equipment status real-time variable set and the paint's own characteristic real-time variable set in the painting workshop; according to the set time interval, collect the vehicle spray effect image data information; compare the vehicle spray effect image data information with the vehicle spray design requirements to obtain a first detection result; generate a coating process deviation evaluation quantitative value according to the collected process environment real-time variable set, the spray equipment status real-time variable set and the paint's own characteristic real-time variable set in the painting workshop, and determine whether the coating process deviation evaluation quantitative value is higher than a preset coating process deviation evaluation threshold to obtain a second detection result; in response to any one of the first detection result and the second detection result not being satisfied, generate a new coating process parameter adaptive control instruction set again according to the process environment real-time variable set, the spray equipment status real-time variable set and the paint's own characteristic real-time variable set to ensure the stability and consistency of the coating process;

[0107] Step S64, record the actual process parameter data and coating effect evaluation results, and feed them back to the coating process parameter optimization model in step S5, so that the model can perform iterative learning and optimization to continuously improve the control accuracy.

[0108] In this step, by parsing and executing the adaptive control instruction set of the coating process parameters, key parameters such as spray pressure, spray gun distance, spray gun angle, atomization particle size and paint flow rate can be accurately adjusted to ensure that the coating process strictly follows the preset optimal process conditions and improves the coating quality and consistency; during the coating operation, the real-time variables of the process environment, equipment state variables and paint characteristic variables are continuously monitored, and the coating effect is evaluated in real time through image recognition, infrared detection and other means; once process deviations or poor coating effects are found, the control instructions can be quickly triggered to be regenerated and adjusted to ensure that the system has the ability to quickly respond to complex environmental changes and equipment status fluctuations; through the linkage of step S6 and step S5, a closed-loop control system with real-time feedback and optimization is formed; the actual executed process parameter data and coating effect evaluation results are fed back to the optimization model for iterative learning and improving the control accuracy, ensuring the self-learning and continuous improvement capabilities of the coating process parameter adaptive control system.

[0109] like Figure 2 As shown, an embodiment of the present invention provides an adaptive control device for automobile coating process parameters. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, it is a hardware architecture diagram of an electronic device where an adaptive control device for automobile coating process parameters is provided in an embodiment of the present invention. Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 2 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the internal memory and runs it.

[0110] like Figure 3 As shown, this embodiment provides an adaptive control device for automobile coating process parameters, comprising:

[0111] The data acquisition module is used to collect the real-time variable set of the process environment in the paint shop, the real-time variable set of the spraying equipment status, and the real-time variable set of the paint's own characteristics;

[0112] The deviation calculation module is used to calculate the deviation between the process environment real-time variable set, the spray equipment state real-time variable set and the paint's own characteristic real-time variable set and the pre-entered process environment standard variable set, the spray equipment state standard variable set and the paint's own characteristic standard variable set, and obtain the coating process real-time deviation multi-modal vector;

[0113] The deviation analysis module is used to input the multi-modal vector of the real-time deviation of the coating process into the pre-built coating process deviation analysis model, and output the coating process deviation evaluation quantitative value;

[0114] A decision control module is used to determine whether the coating process deviation evaluation quantification value is higher than a preset coating process deviation evaluation threshold; when the coating process deviation evaluation quantification value is higher than the preset coating process deviation evaluation threshold, the parameter optimization process is triggered, and the process environment real-time variable set, the spray equipment status real-time variable set and the coating's own characteristic real-time variable set are converted into a coating process real-time characterization data set;

[0115] Parameter optimization module, which is used to input the real-time characterization data set of the coating process into the built-in coating process parameter optimization model and output the coating process parameter adaptive control instruction set;

[0116] The painting execution module adaptively controls the instruction set according to the painting process parameters and executes the corresponding painting operations.

[0117] In this embodiment, the device monitors the environmental conditions, equipment status and paint characteristics of the paint shop in real time through the data acquisition module, ensuring comprehensive and timely data collection of key factors affecting the quality of painting; this enables the device to be accurately controlled based on the latest data, overcoming the limitation of traditional empirical regulation that reacts slowly to environmental changes and equipment status changes; the deviation calculation module calculates the degree of deviation between actual process parameters and standard parameters to form a multi-modal vector of real-time deviation of the coating process; this process realizes dynamic quantitative evaluation of the process status, helps to identify potential problems and give early warning, and avoids coating quality problems caused by the accumulation of small deviations; the deviation analysis module uses a pre-built coating process deviation analysis model to convert the real-time deviation vector into a quantitative value for coating process deviation evaluation; the decision control module decides whether to trigger the parameter optimization process based on the comparison of this quantitative value with the preset threshold; this intelligent analysis and decision-making mechanism enhances the device's ability to judge complex process states, ensuring that parameters are optimized only when necessary. After receiving the real-time characterization data set of the coating process, the parameter optimization module uses the built-in coating process parameter optimization model to calculate the coating process parameter adaptive control instruction set; the instruction set provides the optimal settings of specific parameters such as spray pressure, spray gun position, spray gun angle, atomization particle size and paint flow rate for the current working conditions, realizing the dynamic optimization and precise control of coating process parameters; through the close cooperation of real-time monitoring, precise control, dynamic deviation evaluation, intelligent analysis and decision-making, and adaptive parameter optimization, the device can effectively respond to complex factors such as workshop environment fluctuations, equipment status changes, and coating property changes, reduce the instability of coating effects, significantly improve the quality consistency of automobile coating, and meet consumers' demand for high-quality automobile appearance; real-time and precise process parameter control helps to reduce energy waste and material loss caused by improper parameter settings, such as overspraying and rework, thereby achieving energy conservation and emission reduction, reducing costs, and improving the overall operating efficiency of the coating production line.

[0118] It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on an adaptive control device for automobile coating process parameters. In other embodiments of the present invention, an adaptive control device for automobile coating process parameters may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0119] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0120] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, an adaptive control method for automobile painting process parameters in any embodiment of the present invention is implemented.

[0121] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a method for adaptively controlling automobile painting process parameters in any embodiment of the present invention.

[0122] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0123] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0124] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0125] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0126] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0127] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0128] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptively controlling automobile coating process parameters, characterized in that: The method comprises: Collect the real-time variable set of the process environment in the paint shop, the real-time variable set of the spraying equipment status, and the real-time variable set of the paint's own characteristics; Calculate the deviations between the process environment real-time variable set, the spray equipment state real-time variable set and the paint's own characteristic real-time variable set and the pre-entered process environment standard variable set, the spray equipment state standard variable set and the paint's own characteristic standard variable set, respectively, to obtain a multi-modal vector of the coating process real-time deviation; Input the multi-modal vector of the real-time deviation of the coating process into the pre-built coating process deviation analysis model to obtain the quantitative value of the coating process deviation evaluation; In response to the coating process deviation evaluation quantization value being higher than a preset coating process deviation evaluation threshold, converting a process environment real-time variable set, a spraying equipment state real-time variable set, and a coating material own characteristic real-time variable set into a coating process real-time characterization data set; Input the coating process real-time characterization data set into the pre-built coating process parameter optimization model to obtain the coating process parameter adaptive control instruction set; Execute coating operations according to the coating process parameters adaptively controlling the instruction set; The calculation formula of the coating process deviation analysis model is: ； Among them, Q represents the quantitative value of coating process deviation evaluation, n , m and l The number of variables representing the process environment real-time variable set, the spray equipment status real-time variable set and the coating's own characteristic real-time variable set respectively; , and Respectively represent The real-time variables of the process environment, The real-time variables of the spraying equipment status and Deviation of the real-time variables of the coating's own characteristics; , and They correspond to The real-time variable deviation of the process environment, The real-time variable deviation of the spraying equipment status and the The weight coefficient of the real-time variable of the coating's own characteristics; , and They are the weight coefficients of the process environment real-time variable set, the spraying equipment status real-time variable set and the coating's own characteristic real-time variable set.

2. The method for adaptively controlling automobile coating process parameters according to claim 1, characterized in that: The process environment real-time variable set includes ambient temperature, ambient humidity, air dust particle concentration, wind speed, light intensity and ambient air pressure; The real-time variable set of the spraying equipment status includes motor temperature, motor speed, motor current, motor voltage, fault indication and pumping efficiency; The real-time variable set of the coating's own characteristics includes coating viscosity, solid content, pH value, pigment volume concentration, flash time and coating temperature.

3. The method for adaptively controlling automobile coating process parameters according to claim 2, characterized in that: The coating process parameter adaptive control instruction set includes spray pressure adjustment, spray gun distance adjustment, spray gun angle adjustment, atomization particle size control and paint flow control.

4. The method for adaptively controlling automobile coating process parameters according to claim 3, characterized in that: The multi-modal vector of the real-time deviation of the coating process is: ； Where D represents the multi-modal vector of real-time deviation of the coating process, represents the process environment deviation vector, represents the spraying equipment state deviation vector, Represents the coating's own characteristic deviation vector; They represent the deviation of ambient temperature, ambient humidity, air dust particle concentration, wind speed, light intensity and ambient air pressure respectively; Respectively represent the deviation of motor temperature, motor speed, motor current, motor voltage, fault indication and pumping efficiency; They represent the deviation of coating viscosity, solid content, pH value, pigment volume concentration, flash time and coating temperature respectively.

5. The method for adaptively controlling automobile coating process parameters according to claim 3, characterized in that: After the coating process is executed, the real-time variable set of the process environment in the coating workshop, the real-time variable set of the spraying equipment status and the real-time variable set of the coating's own characteristics are collected according to the set time interval; According to the set time interval, collecting vehicle spraying effect image data information; Compare the vehicle painting effect image data information with the vehicle painting design requirements to obtain a first detection result; Generate a coating process deviation evaluation quantitative value according to the collected process environment real-time variable set, spray equipment status real-time variable set and coating property real-time variable set in the coating workshop, and determine whether the coating process deviation evaluation quantitative value is higher than a preset coating process deviation evaluation threshold value to obtain a second detection result; In response to any one of the first detection result and the second detection result not being satisfied, a coating process parameter adaptive control instruction set is generated again based on the process environment real-time variable set, the spray equipment status real-time variable set and the coating's own characteristic real-time variable set.

6. The method for adaptively controlling automobile coating process parameters according to claim 4, characterized in that: The calculation formula for the deviation of each variable is as follows: ； in, represents the degree of deviation of the variable, Indicates the real-time variable value. Indicates standard value.

7. An adaptive control device for automobile coating process parameters, characterized in that: The device is applied to the method for adaptively controlling automobile coating process parameters as claimed in claim 1, and the device comprises: The data acquisition module is used to collect the real-time variable set of the process environment in the paint shop, the real-time variable set of the spraying equipment status, and the real-time variable set of the paint's own characteristics; The deviation calculation module is used to calculate the deviation between the process environment real-time variable set, the spray equipment state real-time variable set and the paint's own characteristic real-time variable set and the pre-entered process environment standard variable set, the spray equipment state standard variable set and the paint's own characteristic standard variable set, and obtain the coating process real-time deviation multi-modal vector; The deviation analysis module is used to input the multi-modal vector of the real-time deviation of the coating process into the pre-built coating process deviation analysis model, and output the coating process deviation evaluation quantitative value; A decision control module is used to determine whether the coating process deviation evaluation quantification value is higher than a preset coating process deviation evaluation threshold; when the coating process deviation evaluation quantification value is higher than the preset coating process deviation evaluation threshold, the parameter optimization process is triggered, and the process environment real-time variable set, the spray equipment status real-time variable set and the coating's own characteristic real-time variable set are converted into a coating process real-time characterization data set; Parameter optimization module, which is used to input the real-time characterization data set of the coating process into the built-in coating process parameter optimization model and output the coating process parameter adaptive control instruction set; The painting execution module adaptively controls the instruction set according to the painting process parameters and executes the corresponding painting operations.

8. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

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