Intelligent Image Recognition and Inkjet Printing Path Optimization Method and System

Abstract

The present invention discloses an intelligent image recognition and printing path optimization method and system, belonging to the field of printing technology, which includes the following steps: S1: Real-time monitor the printing image data and printing status data, determine the real-time printing data, retrieve and extract features from the real-time printing data, and determine the printing feature data; S2: Construct a printing prediction model, conduct performance tests and optimization adjustments on the printing prediction model, and determine the best printing prediction model after optimization adjustment; S3: Based on the best printing prediction model, conduct prediction and evaluation on the printing feature data. The present invention solves the problems that the existing technology cannot monitor the printing process in real time, cannot control the abnormal printing path in a timely manner, resulting in low printing efficiency and poor printing effect. The present invention can monitor the printing process in real time, can control the abnormal printing path in a timely manner, can optimize the printing path in a timely manner, avoid printing errors, and can improve the printing efficiency and printing effect.

Description

Technical Field

[0001] The present invention relates to the field of printing technology, and in particular to an intelligent image recognition and printing path optimization method and system. Background Art

[0002] With the popularization of automated production lines and the improvement of production efficiency, product identification and packaging are becoming more and more important. However, traditional printing methods can no longer meet the large-scale and rapid product identification needs on modern production lines.

[0003] The Chinese patent with publication number CN103914680B discloses a system and method for identifying and verifying printed characters, wherein: the character generation part includes a character body, a check code part and a connection part. When the system assigns an identification string to the product according to the production characteristics of the product, the character generation part will automatically add the check code part string and the connection part string of the identification string to the back of the character body string according to the identification string, i.e., the character body, so as to generate a character generation string; the character printing part is used to realize the printing of the string on the surface of the product; the character recognition part is used to identify the printed string on the product; the character verification part uses the verification rule to calculate the check code for the recognition result to determine whether the recognition result is correct; it can be used in places in the steel and other industries where it is inconvenient to use the label reader to enter product information, and can effectively ensure the correctness of the recognition code; however, the patent has the following defects:

[0004] When in use, existing printing equipment cannot monitor the printing process in real time, cannot promptly control abnormal printing paths, and cannot promptly optimize printing paths, resulting in printing errors, low printing efficiency, and poor printing effects. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent image recognition and printing path optimization method and system, which can monitor the printing process in real time, promptly control abnormal conditions of the printing path, promptly optimize the printing path, avoid printing errors, and improve printing efficiency and printing effects, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent image recognition and printing path optimization method comprises the following steps:

[0008] S1: monitor the printing image data and printing status data in real time, determine the real-time printing data based on intelligent image recognition, retrieve and extract features of the real-time printing data, and determine the printing feature data;

[0009] S2: According to the printing path optimization requirements, a printing prediction model is constructed, and the performance of the printing prediction model is tested and optimized to determine the best printing prediction model after optimization and adjustment;

[0010] S3: Based on the best printing prediction model after optimization and adjustment, predict and evaluate the printing feature data, formulate a printing path optimization plan, and perform intelligent optimization and control of the printing path.

[0011] According to another aspect of the present invention, there is provided an intelligent image recognition and printing path optimization system for implementing the intelligent image recognition and printing path optimization method as described above, comprising:

[0012] The printing monitoring module is used to monitor the printing image data and printing status data in real time, and determine the real-time printing data based on intelligent image recognition;

[0013] A data processing module is used to process the real-time data of printing based on intelligent image recognition and determine the characteristic data of printing based on intelligent image recognition;

[0014] A model building module is used to train a suitable model architecture selected based on a training set to determine a printing prediction model;

[0015] An adjustment and optimization module is used to perform performance testing and optimization on the printing prediction model to determine the best printing prediction model after optimization and adjustment;

[0016] A prediction and evaluation module is used to predict and evaluate the printing feature data based on intelligent image recognition, and determine the prediction and evaluation results of the printing based on intelligent image recognition;

[0017] The printing control module is used to formulate a printing path optimization plan and perform intelligent optimization and control of the printing path.

[0018] Preferably, the printing monitoring module includes:

[0019] An image monitoring unit is used to perform real-time monitoring and intelligent image recognition of the image printed by the printing device during the printing process to determine the printing image data;

[0020] The status monitoring unit is used to monitor the pressure, temperature, flow rate and speed of the printing equipment in real time during the printing process to determine the printing status data;

[0021] Among them, based on the printing image data and the printing status data, the real-time printing data based on intelligent image recognition is determined.

[0022] Preferably, the printing monitoring module further includes:

[0023] A flow rate real-time monitoring module, used for real-time monitoring of the volume of paint sprayed corresponding to a unit spraying cycle of the nozzle of the printing device; wherein the unit spraying cycle is 3s-5s;

[0024] The spraying coefficient acquisition module is used to obtain the spraying coefficient according to the volume of the paint sprayed corresponding to the unit spraying cycle of the nozzle of the printing device; wherein the spraying coefficient is obtained by the following formula:

[0025]

[0026] Wherein, P represents the volume acquisition spraying coefficient; n represents the number of unit spraying cycles experienced by the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; w represents the adjustment coefficient. When , let w be the maximum blockage ratio allowed by the current nozzle; when When w=1, p represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0027]

[0028] Wherein, p represents the adjustment coefficient; n represents the number of unit spraying cycles experienced by the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; t represents the duration of the unit spraying cycle; Q m It indicates the target flow value corresponding to the unit time of the printing equipment, and the unit time is 1s;

[0029] A coefficient parameter comparison module, used for comparing the spraying coefficient with a preset spraying coefficient threshold;

[0030] A printing parameter retrieving module, used for retrieving the speed, temperature and pressure corresponding parameters in the printing process when the spraying coefficient exceeds a preset spraying coefficient threshold;

[0031] The spraying operation evaluation module is used to evaluate the current operation quality of the printing equipment using the corresponding parameters of speed, temperature and pressure in the printing process.

[0032] Preferably, the spraying operation evaluation module includes:

[0033] An actual parameter value extraction module is used to extract the pressure value, temperature value and speed value of the current actual operation of the printing device when the spraying coefficient exceeds a preset spraying coefficient threshold;

[0034] A standard parameter value extraction module is used to extract the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device;

[0035] The operation evaluation parameter acquisition module is used to obtain the operation evaluation parameters of the printing device according to the pressure value, temperature value and speed value of the current actual operation of the printing device combined with the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; wherein the operation evaluation parameters are obtained by the following formula:

[0036]

[0037] Where K represents the operating evaluation parameter of the printing equipment; F s , W s and V s They respectively represent the actual operating pressure, temperature and speed of the printing equipment; F m , W m and V m They represent the target pressure value, target temperature value and target speed value V corresponding to the current printing task of the printing device. i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; k 01 , k 02 and k 03 Respectively represent the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient, and the values ​​of the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient are as follows:

[0038]

[0039] Wherein, n represents the number of unit spraying cycles experienced by the printing device; F s , W s and V s They respectively represent the actual operating pressure, temperature and speed of the printing equipment; F m , W m and V m They respectively represent the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; V irepresents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle;

[0040] The abnormality determination module is used to determine that the operation of the current printing device is abnormal and issue an abnormality alarm when the operation evaluation parameter of the printing device is lower than a preset first operation evaluation parameter threshold, or exceeds a preset second operation evaluation parameter threshold.

[0041] Preferably, the data processing module includes:

[0042] A data retrieval unit, used to retrieve the real-time data of printing based on intelligent image recognition;

[0043] Obtain real-time printing data based on intelligent image recognition;

[0044] Based on the sequential retrieval method, the real-time data of printing based on intelligent image recognition is retrieved;

[0045] Check the consistency of real-time data printed based on intelligent image recognition;

[0046] According to the reasonable value range and mutual relationship of each variable in the real-time printing data;

[0047] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time printing data;

[0048] Process invalid and missing values ​​of real-time printing data based on intelligent image recognition;

[0049] Remove invalid and missing data in real-time printing data that is of no value to printing path optimization;

[0050] Determine the real-time printing data that is valuable for optimizing the printing path;

[0051] A feature extraction unit, used for extracting features from the retrieved real-time printing data;

[0052] Acquire the real-time printing data which is valuable for optimizing the printing path after retrieval;

[0053] Extract features from the real-time printing data that is valuable for optimizing the printing path after retrieval;

[0054] Determine the printing feature data based on intelligent image recognition.

[0055] Preferably, the model building module includes:

[0056] A data collection unit, used for collecting printing history data;

[0057] According to the requirements of printing path optimization, the historical printing images and historical printing status of the printing equipment during the printing process are collected to determine the historical printing data;

[0058] A data division unit, used for dividing the printing history data;

[0059] Get printing history data;

[0060] Divide the printing history data;

[0061] Determine the training set and test set;

[0062] The architecture selection unit is used to select the appropriate model architecture;

[0063] According to the printing path optimization requirements, select the appropriate model architecture based on the training set;

[0064] Model training unit, used to build a printing prediction model;

[0065] Get the training set;

[0066] Get the appropriate model architecture selected;

[0067] Based on the training set, train the selected appropriate model architecture;

[0068] A printing prediction model is determined.

[0069] Preferably, the adjustment and optimization module includes:

[0070] A performance testing unit, used to perform performance testing on the printing prediction model;

[0071] Get the test set;

[0072] Obtaining a printing prediction model;

[0073] Perform performance test on the printing prediction model based on the test set;

[0074] Determine performance test results based on the printing prediction model;

[0075] An optimization and adjustment unit, used for optimizing and adjusting the printing prediction model;

[0076] Obtain performance test results based on the printing prediction model;

[0077] Conduct in-depth exploration and correlation analysis of performance test results based on the printing prediction model;

[0078] Determine the optimization adjustment plan based on the printing prediction model;

[0079] Optimize and adjust the printing prediction model based on the optimization adjustment plan;

[0080] Determine the best printing prediction model after optimization and adjustment.

[0081] Preferably, the prediction and evaluation module includes:

[0082] A data extraction unit, used for extracting printing feature data based on intelligent image recognition;

[0083] Extract the printing feature data based on intelligent image recognition according to the printing path optimization requirements; the prediction and evaluation unit is used to predict and evaluate the printing feature data based on intelligent image recognition; obtain the best printing prediction model after optimization and adjustment;

[0084] Input the extracted printing feature data based on intelligent image recognition into the best printing prediction model after optimization and adjustment;

[0085] Based on the best printing prediction model after optimization and adjustment, the printing feature data based on intelligent image recognition is predicted and evaluated;

[0086] Determine the prediction and evaluation results of printing based on intelligent image recognition;

[0087] Among them, the printing prediction evaluation results include normal printing path and abnormal printing path.

[0088] Preferably, the printing control module includes:

[0089] Strategy formulation unit, used to formulate printing path optimization plan;

[0090] Obtain printing prediction and evaluation results based on intelligent image recognition;

[0091] Conduct in-depth exploration and relevant analysis of the prediction and evaluation results of printing based on intelligent image recognition;

[0092] When the printing path is abnormal, formulate a printing path optimization plan based on intelligent image recognition;

[0093] Printing control unit, used for intelligent optimization and control of printing paths;

[0094] Obtain printing path optimization solutions based on intelligent image recognition;

[0095] Intelligent optimization and control of the printing path is carried out based on the printing path optimization solution.

[0096] Compared with the prior art, the present invention has the following beneficial effects:

[0097] The present invention monitors the printing image data and the printing status data in real time, determines the real-time printing data based on intelligent image recognition, searches and extracts features of the real-time printing data, determines the printing feature data, constructs a printing prediction model according to the printing path optimization requirements, performs performance testing and optimization adjustment on the printing prediction model, determines the best printing prediction model after optimization and adjustment, predicts and evaluates the printing feature data based on the best printing prediction model after optimization and adjustment, formulates a printing path optimization plan, and performs intelligent optimization and control of the printing path. The printing process can be monitored in real time, abnormal conditions of the printing path can be timely controlled, the printing path can be timely optimized, printing errors can be avoided, and the printing efficiency and printing effect can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 It is a flow chart of the intelligent image recognition and printing path optimization method of the present invention;

[0099] Figure 2 It is a structural diagram of the intelligent image recognition and printing path optimization system of the present invention. DETAILED DESCRIPTION

[0100] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0101] In order to solve the problem that the existing printing equipment cannot monitor the printing process in real time, cannot timely control the abnormal situation of the printing path, and cannot timely optimize the printing path, resulting in printing errors, low printing efficiency and poor printing effect, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0102] The intelligent image recognition and printing path optimization system includes: a printing monitoring module, a data processing module, a model building module, an adjustment and optimization module, a prediction and evaluation module and a printing control module.

[0103] It should be noted that through the interactive communication between the printing monitoring module, data processing module, model building module, adjustment and optimization module, prediction and evaluation module and printing control module, the printing process can be monitored in real time, abnormal printing path conditions can be timely controlled, the printing path can be timely optimized to avoid printing errors, and the printing efficiency and printing effect can be improved.

[0104] Among them, the printing monitoring module is used to monitor the printing image data and printing status data in real time, and determine the real-time printing data based on intelligent image recognition;

[0105] In this embodiment, the printing monitoring module includes:

[0106] An image monitoring unit is used to perform real-time monitoring and intelligent image recognition of the image printed by the printing device during the printing process to determine the printing image data;

[0107] It should be noted that by real-time monitoring of the image printed by the printing device during the printing process, it can be quickly discovered whether the image printed by the printing device is correct. If the image path printed by the printing device is abnormal, the image path printed by the printing device can be optimized and adjusted to avoid printing errors.

[0108] The status monitoring unit is used to monitor the pressure, temperature, flow rate and speed of the printing equipment in real time during the printing process to determine the printing status data;

[0109] It should be noted that by real-time monitoring of the pressure, temperature, flow rate and speed of the printing equipment during the printing process, it is possible to quickly discover whether the printing equipment is in a normal working state. If the working state of the printing equipment is abnormal, the printing equipment can be maintained in time to ensure that the printing equipment can work quickly and normally.

[0110] Among them, based on the printing image data and the printing status data, the real-time printing data based on intelligent image recognition is determined.

[0111] Specifically, the printing monitoring module further includes:

[0112] A flow rate real-time monitoring module, used for real-time monitoring of the volume of paint sprayed corresponding to a unit spraying cycle of the nozzle of the printing device; wherein the unit spraying cycle is 3s-5s;

[0113] The spraying coefficient acquisition module is used to obtain the spraying coefficient according to the volume of the paint sprayed corresponding to the unit spraying cycle of the nozzle of the printing device; wherein the spraying coefficient is obtained by the following formula:

[0114]

[0115] Wherein, P represents the volume acquisition spraying coefficient; n represents the number of unit spraying cycles experienced by the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h irepresents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; w represents the adjustment coefficient. When , let w be the maximum blockage ratio allowed by the current nozzle; when When w=1, p represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0116]

[0117] Wherein, p represents the adjustment coefficient; n represents the number of unit spraying cycles experienced by the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; t represents the duration of the unit spraying cycle; Q m It indicates the target flow value corresponding to the unit time of the printing equipment, and the unit time is 1s;

[0118] A coefficient parameter comparison module, used for comparing the spraying coefficient with a preset spraying coefficient threshold;

[0119] A printing parameter retrieving module, used for retrieving the speed, temperature and pressure corresponding parameters in the printing process when the spraying coefficient exceeds a preset spraying coefficient threshold;

[0120] The spraying operation evaluation module is used to evaluate the current operation quality of the printing equipment using the corresponding parameters of speed, temperature and pressure in the printing process.

[0121] The technical effect of the above technical solution is: through the real-time flow monitoring module, the volume of paint sprayed by the nozzle of the printing equipment within a unit spraying cycle (3s-5s) can be accurately monitored, which helps to achieve real-time monitoring and precise control of the spraying process. This real-time monitoring mechanism can ensure the stability and consistency of the spraying process and reduce quality problems caused by fluctuations in the spraying amount. The spraying coefficient acquisition module dynamically calculates the spraying coefficient based on factors such as the spraying volume, target spraying area, target spraying thickness, and the blockage ratio of the nozzle. This dynamic adjustment mechanism can reflect the actual working status of the printing equipment, especially when there is a risk of blockage in the nozzle. By introducing the adjustment coefficient w, the spraying efficiency and quality can be more accurately evaluated.

[0122] The coefficient parameter comparison module compares the calculated spray coefficient with the preset threshold. When the spray coefficient exceeds the threshold, it indicates that there may be an abnormality in the printing process. At this time, the printing parameter retrieval module will automatically retrieve key parameters such as speed, temperature and pressure in the printing process to provide a basis for subsequent troubleshooting and performance optimization. This intelligent early warning mechanism helps to discover and solve problems in a timely manner, improving production efficiency and product quality.

[0123] The spray operation evaluation module uses the retrieved parameters such as speed, temperature and pressure to evaluate the current operation quality of the printing equipment. Through the evaluation results, the key factors affecting the spray quality can be analyzed, and the parameters can be adjusted and optimized accordingly. This closed-loop feedback mechanism helps to continuously improve the operating stability and spray quality of the printing equipment. The entire technical solution improves the production efficiency and product quality of the printing equipment through real-time monitoring, dynamic adjustment, intelligent early warning and evaluation optimization. At the same time, by timely discovering and solving problems, production losses caused by downtime for maintenance are reduced, further reducing production costs.

[0124] In summary, this technical solution achieves efficient, stable and precise operation of printing equipment through a series of intelligent monitoring, evaluation and optimization methods, which is of great significance for improving product quality and reducing production costs.

[0125] Specifically, the spraying operation evaluation module includes:

[0126] An actual parameter value extraction module is used to extract the pressure value, temperature value and speed value of the current actual operation of the printing device when the spraying coefficient exceeds a preset spraying coefficient threshold;

[0127] A standard parameter value extraction module is used to extract the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device;

[0128] The operation evaluation parameter acquisition module is used to obtain the operation evaluation parameters of the printing device according to the pressure value, temperature value and speed value of the current actual operation of the printing device combined with the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; wherein the operation evaluation parameters are obtained by the following formula:

[0129]

[0130] Where K represents the operating evaluation parameter of the printing equipment; F s , W s and V s Respectively represent the actual operating pressure value, temperature value and speed value of the printing equipment; F m , W m and V m They represent the target pressure value, target temperature value and target speed value V corresponding to the current printing task of the printing device. i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; k01 , k 02 and k 03 Respectively represent the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient, and the values ​​of the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient are as follows:

[0131]

[0132] Wherein, n represents the number of unit spraying cycles experienced by the printing device; F s , W s and V s Respectively represent the actual operating pressure value, temperature value and speed value of the printing equipment; F m , W m and V m They respectively represent the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle;

[0133] The abnormality determination module is used to determine that the operation of the current printing device is abnormal and issue an abnormality alarm when the operation evaluation parameter of the printing device is lower than a preset first operation evaluation parameter threshold, or exceeds a preset second operation evaluation parameter threshold.

[0134] The technical effect of the above technical solution is: through the actual parameter value extraction module and the standard parameter value extraction module, the pressure, temperature and speed values ​​of the current actual operation of the printing equipment can be accurately obtained, and compared with the target values ​​set for the printing task. This refined data extraction method provides a solid foundation for subsequent operation evaluation. The operation evaluation parameter acquisition module calculates a comprehensive operation evaluation parameter K by comprehensively considering the difference between the actual operation parameters and the target parameters, as well as factors such as volume, area and thickness during the spraying process. This parameter can fully reflect the operating status and performance of the printing equipment in the current printing task. At the same time, when calculating the operation evaluation parameters, the first adjustment coefficient k01, the second adjustment coefficient k02 and the third adjustment coefficient k03 are introduced, and these coefficients are dynamically adjusted according to the actual operation of the printing equipment. This design makes the operation evaluation more flexible and accurate, and can better adapt to changes in different printing tasks and equipment status.

[0135] The abnormality determination module can promptly detect abnormal conditions in the operation of the printing equipment by comparing the operation evaluation parameters with the preset thresholds. Once it is determined to be abnormal, an abnormal alarm will be issued to remind the operator to take timely measures to deal with it. This mechanism helps to avoid the expansion of equipment failures and ensure production safety and product quality. Through real-time monitoring, accurate evaluation and timely alarm, this technical solution can ensure that the printing equipment operates in the best condition and reduce production interruptions and quality problems caused by equipment failure or performance degradation. At the same time, by continuously optimizing and adjusting the equipment operating parameters, production efficiency and product quality can be further improved. Timely detection and handling of equipment abnormalities can reduce downtime and maintenance costs caused by equipment failure. In addition, through accurate operation evaluation and parameter adjustment, the service life of the equipment can be extended and the overall maintenance cost can be reduced.

[0136] In summary, this technical solution achieves comprehensive monitoring and accurate evaluation of the operating status of the printing equipment through mechanisms such as refined operation evaluation, dynamic adjustment coefficient, abnormal judgment and alarm, which helps to improve production efficiency and product quality and reduce maintenance costs.

[0137] The data processing module is used to process the real-time data of the printing based on intelligent image recognition and determine the characteristic data of the printing based on intelligent image recognition;

[0138] In this embodiment, the data processing module includes:

[0139] A data retrieval unit, used to retrieve the real-time data of printing based on intelligent image recognition;

[0140] Obtain real-time printing data based on intelligent image recognition;

[0141] Based on the sequential retrieval method, the real-time data of printing based on intelligent image recognition is retrieved;

[0142] Check the consistency of real-time data printed based on intelligent image recognition;

[0143] According to the reasonable value range and mutual relationship of each variable in the real-time printing data;

[0144] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time printing data;

[0145] Process invalid and missing values ​​of real-time printing data based on intelligent image recognition;

[0146] Remove invalid and missing data in real-time printing data that is of no value to printing path optimization;

[0147] Determine the real-time printing data that is valuable for optimizing the printing path;

[0148] A feature extraction unit, used for extracting features from the retrieved real-time printing data;

[0149] Acquire the real-time printing data which is valuable for optimizing the printing path after retrieval;

[0150] Extract features from the real-time printing data that is valuable for optimizing the printing path after retrieval;

[0151] Determine the printing feature data based on intelligent image recognition.

[0152] The model building module is used to train the selected appropriate model architecture based on the training set to determine the printing prediction model;

[0153] In this embodiment, the model building module includes:

[0154] A data collection unit, used for collecting printing history data;

[0155] According to the requirements of printing path optimization, the historical printing images and historical printing status of the printing equipment during the printing process are collected to determine the historical printing data;

[0156] A data division unit, used for dividing the printing history data;

[0157] Get printing history data;

[0158] Divide the printing history data;

[0159] Determine the training set and test set;

[0160] The architecture selection unit is used to select the appropriate model architecture;

[0161] According to the printing path optimization requirements, select the appropriate model architecture based on the training set;

[0162] Model training unit, used to build a printing prediction model;

[0163] Get the training set;

[0164] Get the appropriate model architecture selected;

[0165] Based on the training set, train the selected appropriate model architecture;

[0166] A printing prediction model is determined.

[0167] Among them, the adjustment and optimization module is used to perform performance testing and optimization adjustment on the printing prediction model, and determine the best printing prediction model after optimization and adjustment;

[0168] In this embodiment, the adjustment and optimization module includes:

[0169] A performance testing unit, used to perform performance testing on the printing prediction model;

[0170] Get the test set;

[0171] Obtaining a printing prediction model;

[0172] Perform performance test on the printing prediction model based on the test set;

[0173] Determine performance test results based on the printing prediction model;

[0174] An optimization and adjustment unit, used for optimizing and adjusting the printing prediction model;

[0175] Obtain performance test results based on the printing prediction model;

[0176] Conduct in-depth exploration and correlation analysis of performance test results based on the printing prediction model;

[0177] Determine the optimization adjustment plan based on the printing prediction model;

[0178] Optimize and adjust the printing prediction model based on the optimization adjustment plan;

[0179] Determine the best printing prediction model after optimization and adjustment.

[0180] The prediction and evaluation module is used to predict and evaluate the printing feature data based on intelligent image recognition, and determine the prediction and evaluation results of the printing based on intelligent image recognition;

[0181] In this embodiment, the prediction and evaluation module includes:

[0182] A data extraction unit, used for extracting printing feature data based on intelligent image recognition;

[0183] According to the requirements of printing path optimization, the printing feature data based on intelligent image recognition is extracted;

[0184] A prediction and evaluation unit, used for predicting and evaluating printing feature data based on intelligent image recognition;

[0185] Obtain the best printing prediction model after optimization and adjustment;

[0186] Input the extracted printing feature data based on intelligent image recognition into the best printing prediction model after optimization and adjustment;

[0187] Based on the best printing prediction model after optimization and adjustment, the printing feature data based on intelligent image recognition is predicted and evaluated;

[0188] Determine the prediction and evaluation results of printing based on intelligent image recognition;

[0189] Among them, the printing prediction evaluation results include normal printing path and abnormal printing path.

[0190] Among them, the printing control module is used to formulate a printing path optimization plan and perform intelligent optimization and control of the printing path.

[0191] In this embodiment, the printing control module includes:

[0192] Strategy formulation unit, used to formulate printing path optimization plan;

[0193] Obtain printing prediction and evaluation results based on intelligent image recognition;

[0194] Conduct in-depth exploration and relevant analysis of the prediction and evaluation results of printing based on intelligent image recognition;

[0195] When the printing path is abnormal, formulate a printing path optimization plan based on intelligent image recognition;

[0196] Printing control unit, used for intelligent optimization and control of printing paths;

[0197] Obtain printing path optimization solutions based on intelligent image recognition;

[0198] Intelligent optimization and control of the printing path is carried out based on the printing path optimization solution.

[0199] Therefore, by real-time monitoring of the printing image data and printing status data, determining the real-time printing data based on intelligent image recognition, retrieving and extracting features of the real-time printing data, determining the printing feature data, and constructing a printing prediction model according to the printing path optimization requirements. The performance of the printing prediction model is tested and optimized, and the best printing prediction model after optimization and adjustment is determined. Based on the best printing prediction model after optimization and adjustment, the printing feature data is predicted and evaluated, a printing path optimization plan is formulated, and the printing path is intelligently optimized and controlled. The printing process can be monitored in real time, abnormal conditions of the printing path can be controlled in a timely manner, the printing path can be optimized in a timely manner, printing errors can be avoided, and printing efficiency and printing effects can be improved.

[0200] In order to better demonstrate the intelligent image recognition and printing path optimization process, this embodiment now provides an intelligent image recognition and printing path optimization method, which is implemented based on the above-mentioned intelligent image recognition and printing path optimization system, and includes the following steps:

[0201] S1: monitor the printing image data and printing status data in real time, determine the real-time printing data based on intelligent image recognition, retrieve and extract features of the real-time printing data, and determine the printing feature data;

[0202] S2: According to the printing path optimization requirements, a printing prediction model is constructed, and the performance of the printing prediction model is tested and optimized to determine the best printing prediction model after optimization and adjustment;

[0203] S3: Based on the best printing prediction model after optimization and adjustment, predict and evaluate the printing feature data, formulate a printing path optimization plan, and perform intelligent optimization and control of the printing path.

[0204] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Intelligent image recognition and printing path optimization method, characterized in that: The steps include: S1: monitor the printing image data and printing status data in real time, determine the real-time printing data based on intelligent image recognition, retrieve and extract features of the real-time printing data, and determine the printing feature data; Monitor the pressure, temperature, flow rate and speed of the printing equipment in real time during the printing process to determine the printing status data; Real-time monitoring of the volume of paint sprayed corresponding to a unit spraying cycle of the nozzle of the printing device; wherein the unit spraying cycle is 3s-5s; and obtaining a spraying coefficient according to the volume of paint sprayed corresponding to the unit spraying cycle of the nozzle of the printing device; comparing the spraying coefficient with a preset spraying coefficient threshold; When the spraying coefficient exceeds a preset spraying coefficient threshold, the speed, temperature and pressure corresponding parameters in the printing process are retrieved; the current operation quality of the printing device is evaluated using the speed, temperature and pressure corresponding parameters in the printing process; S2: According to the printing path optimization requirements, a printing prediction model is constructed, and the performance of the printing prediction model is tested and optimized to determine the best printing prediction model after optimization and adjustment; S3: Based on the best printing prediction model after optimization and adjustment, predict and evaluate the printing feature data, formulate a printing path optimization plan, and perform intelligent optimization and control of the printing path.

2. An intelligent image recognition and printing path optimization system, used to implement the intelligent image recognition and printing path optimization method as claimed in claim 1, characterized in that: include: The printing monitoring module is used to monitor the printing image data and printing status data in real time, and determine the real-time printing data based on intelligent image recognition; A data processing module is used to process the real-time data of printing based on intelligent image recognition and determine the characteristic data of printing based on intelligent image recognition; A model building module is used to train a suitable model architecture selected based on a training set to determine a printing prediction model; An adjustment and optimization module is used to perform performance testing and optimization on the printing prediction model to determine the best printing prediction model after optimization and adjustment; A prediction and evaluation module is used to predict and evaluate the printing feature data based on intelligent image recognition, and determine the prediction and evaluation results of the printing based on intelligent image recognition; The printing control module is used to formulate a printing path optimization plan and perform intelligent optimization and control of the printing path.

3. The intelligent image recognition and printing path optimization system according to claim 2, characterized in that: The printing monitoring module comprises: An image monitoring unit is used to perform real-time monitoring and intelligent image recognition of the image printed by the printing device during the printing process to determine the printing image data; The status monitoring unit is used to monitor the pressure, temperature, flow rate and speed of the printing equipment in real time during the printing process to determine the printing status data; Among them, based on the printing image data and the printing status data, the real-time printing data based on intelligent image recognition is determined.

4. The intelligent image recognition and printing path optimization system according to claim 3, characterized in that: The printing monitoring module further includes: A flow rate real-time monitoring module, used for real-time monitoring of the volume of paint sprayed corresponding to a unit spraying cycle of the nozzle of the printing device; wherein the unit spraying cycle is 3s-5s; The spraying coefficient acquisition module is used to obtain the spraying coefficient according to the volume of the paint sprayed corresponding to the unit spraying cycle of the nozzle of the printing device; wherein the spraying coefficient is obtained by the following formula: Wherein, P represents the volume acquisition spray coefficient; n represents the number of unit spray cycles experienced by the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; w represents the adjustment coefficient. When , let w be the maximum blockage ratio allowed by the current nozzle; when When w=1, p represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: Wherein, p represents the adjustment coefficient; n represents the number of unit spraying cycles experienced by the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; t represents the duration of the unit spraying cycle; Q m It indicates the target flow value corresponding to the unit time of the printing equipment, and the unit time is 1s; A coefficient parameter comparison module, used for comparing the spraying coefficient with a preset spraying coefficient threshold; A printing parameter retrieving module, used for retrieving the speed, temperature and pressure corresponding parameters in the printing process when the spraying coefficient exceeds a preset spraying coefficient threshold; The spraying operation evaluation module is used to evaluate the current operation quality of the printing equipment using the corresponding parameters of speed, temperature and pressure in the printing process.

5. The intelligent image recognition and printing path optimization system according to claim 4, characterized in that: The spray operation evaluation module includes: An actual parameter value extraction module is used to extract the pressure value, temperature value and speed value of the current actual operation of the printing device when the spraying coefficient exceeds a preset spraying coefficient threshold; A standard parameter value extraction module is used to extract the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; The operation evaluation parameter acquisition module is used to obtain the operation evaluation parameters of the printing device according to the pressure value, temperature value and speed value of the current actual operation of the printing device combined with the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; wherein the operation evaluation parameters are obtained by the following formula: Where K represents the operating evaluation parameter of the printing equipment; F s , W s and V s Respectively represent the actual operating pressure value, temperature value and speed value of the printing equipment; F m , W m and V m They represent the target pressure value, target temperature value and target speed value V corresponding to the current printing task of the printing device. i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; k 01 , k 02 and k 03 Respectively represent the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient, and the values ​​of the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient are as follows: Wherein, n represents the number of unit spraying cycles experienced by the printing device; F s , W s and V s Respectively represent the actual operating pressure value, temperature value and speed value of the printing equipment; F m , W m and V m They respectively represent the target pressure value, target temperature value and target speed value corresponding to the current printing task of the printing device; V i represents the volume of paint sprayed corresponding to the i-th unit spraying cycle; S i represents the target spraying area corresponding to the i-th unit spraying cycle; h i represents the target spraying thickness of the target spraying area corresponding to the i-th unit spraying cycle; The abnormality determination module is used to determine that the operation of the current printing device is abnormal and issue an abnormality alarm when the operation evaluation parameter of the printing device is lower than a preset first operation evaluation parameter threshold, or exceeds a preset second operation evaluation parameter threshold.

6. The intelligent image recognition and printing path optimization system according to claim 3, characterized in that: The data processing module comprises: A data retrieval unit, used to retrieve the real-time data of printing based on intelligent image recognition; Obtain real-time printing data based on intelligent image recognition; Based on the sequential retrieval method, the real-time data of printing based on intelligent image recognition is retrieved; Check the consistency of real-time data printed based on intelligent image recognition; According to the reasonable value range and mutual relationship of each variable in the real-time printing data; Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time printing data; Process invalid and missing values ​​of real-time printing data based on intelligent image recognition; Remove invalid and missing data in real-time printing data that is of no value to printing path optimization; Determine the real-time printing data that is valuable for optimizing the printing path; A feature extraction unit, used for extracting features from the retrieved real-time printing data; Acquire the real-time printing data which is valuable for optimizing the printing path after retrieval; Extract features from the real-time printing data that is valuable for optimizing the printing path after retrieval; Determine the printing feature data based on intelligent image recognition.

7. The intelligent image recognition and printing path optimization system according to claim 4, characterized in that: The model building module includes: A data collection unit, used for collecting printing history data; According to the requirements of printing path optimization, the historical printing images and historical printing status of the printing equipment during the printing process are collected to determine the historical printing data; A data division unit, used for dividing the printing history data; Get printing history data; Divide the printing history data; Determine the training set and test set; The architecture selection unit is used to select the appropriate model architecture; According to the printing path optimization requirements, select the appropriate model architecture based on the training set; Model training unit, used to build a printing prediction model; Get the training set; Get the appropriate model architecture selected; Based on the training set, train the selected appropriate model architecture; A printing prediction model is determined.

8. The intelligent image recognition and printing path optimization system according to claim 7, characterized in that: The adjustment and optimization module comprises: A performance testing unit, used to perform performance testing on the printing prediction model; Get the test set; Obtaining a printing prediction model; Perform performance test on the printing prediction model based on the test set; Determine performance test results based on the printing prediction model; An optimization and adjustment unit, used for optimizing and adjusting the printing prediction model; Obtain performance test results based on the printing prediction model; Conduct in-depth exploration and correlation analysis of performance test results based on the printing prediction model; Determine the optimization adjustment plan based on the printing prediction model; Optimize and adjust the printing prediction model based on the optimization adjustment plan; Determine the best printing prediction model after optimization and adjustment.

9. The intelligent image recognition and printing path optimization system according to claim 8, characterized in that: The prediction and evaluation module comprises: A data extraction unit, used for extracting printing feature data based on intelligent image recognition; According to the requirements of printing path optimization, the printing feature data based on intelligent image recognition is extracted; A prediction and evaluation unit, used for predicting and evaluating printing feature data based on intelligent image recognition; Obtain the best printing prediction model after optimization and adjustment; Input the extracted printing feature data based on intelligent image recognition into the best printing prediction model after optimization and adjustment; Based on the best printing prediction model after optimization and adjustment, the printing feature data based on intelligent image recognition is predicted and evaluated; Determine the prediction and evaluation results of printing based on intelligent image recognition; Among them, the printing prediction evaluation results include normal printing path and abnormal printing path.

10. The intelligent image recognition and printing path optimization system according to claim 9, characterized in that: The printing control module includes: Strategy formulation unit, used to formulate printing path optimization plan; Obtain printing prediction and evaluation results based on intelligent image recognition; Conduct in-depth exploration and relevant analysis of the prediction and evaluation results of printing based on intelligent image recognition; When the printing path is abnormal, formulate a printing path optimization plan based on intelligent image recognition; Printing control unit, used for intelligent optimization and control of printing paths; Obtain printing path optimization solutions based on intelligent image recognition; Intelligent optimization and control of the printing path is carried out based on the printing path optimization solution.

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