Label boundary automatic identification system based on reflective sensor

Abstract

The invention discloses an automatic label boundary identification system based on a reflective sensor, and relates to the technical field of printers. Comprising a label boundary automatic learning module, a printing operation module, a real-time automatic correction module, a data recording and analyzing module and a control and user interface module. According to the invention, physical characteristics of different label paper consumables are learned and identified by using the reflective sensor, and the learned values are automatically recorded and stored in the SPIFLASH, so that surface data of the label paper consumables can be collected in real time in the printing operation, the real-time data are compared with the learned values, error values are calculated, and the printing efficiency is improved. The position of the X-direction servo motor and the position of the Y-direction servo motor are adjusted in real time after the proportionality coefficient is adjusted, accurate alignment of ink jet drop points is ensured, the real-time automatic correction function starts from the second PASS of the printing operation and continues till the operation is completed, the rejection rate caused by position deviation is remarkably reduced, ink consumption is reduced, and the production cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of printers, and in particular to an automatic label boundary recognition system based on a reflective sensor. Background Art

[0002] With the rapid development of the logistics industry and manufacturing, label paper is increasingly used as an information carrier. However, the traditional label paper printing method relies on manual typesetting and adjustment, which has many disadvantages: first, manual operation can easily lead to deviations in the label position, which not only affects printing efficiency, but also reduces the quality of the label; second, due to problems such as position deviation, the scrap rate is high, resulting in a waste of resources; third, the traditional method consumes a lot of ink, which increases production costs; finally, because it does not conform to the green and environmentally friendly production concept, waste disposal is difficult, which imposes a burden on the environment. Summary of the invention

[0003] The purpose of the present invention is to provide a label boundary automatic recognition system based on a reflective sensor, which is used to solve the problems existing in the traditional label printing method, improve the efficiency and accuracy of label printing, and reduce resource consumption.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] The present application provides a label boundary automatic recognition system based on a reflective sensor, including a label boundary automatic learning module, a printing operation module, a real-time automatic correction module, a data recording and analysis module, and a control and user interface module.

[0006] The label boundary automatic learning module learns the physical characteristics of different label paper consumables through a reflective sensor, identifies the boundary of each label, and automatically records and saves the learning value in SPIFLASH;

[0007] The printing job module converts the image source file into the raster dot matrix image required by the printer using the RIP tool, and imports the label data for printing, automatically records the printing data, displays a real-time progress bar, and records the number of labels to generate a log;

[0008] The real-time automatic correction module collects the surface data of the label paper consumables in real time during the printing operation, calculates the error by comparing the real-time data with the learning value, and adjusts the X-direction and Y-direction servo motors in real time, and enters the real-time automatic correction from the second PASS of the printing operation;

[0009] The data recording and analysis module sends and records the printing data to the printing system in real time via RS485, displays a real-time progress bar on the interface, and records the number of labels generated in the log;

[0010] The control and user interface module includes an operation panel, through which learning in the X and Y directions is performed, a printing job is started, and the sensitivity of the real-time automatic correction module is adjusted, wherein the operation panel displays "array spacing" information in the X and Y directions.

[0011] Furthermore, before learning the material differences, surface flatness and typesetting errors of different label paper consumables through the reflective sensor, it includes: installing the label paper consumables into the printer, then starting the learning program through the operation panel, and then performing X-direction learning, driving the spray car to automatically move in the X direction and collect data of 6 label distances 3 times, comparing the collected array information with the actual consumables information, calculating the error, and adjusting it until the error is eliminated to zero, and then storing the learning value in SPIFLASH; according to the X-direction learning result, performing Y-direction learning, similarly collecting and comparing the array information, calculating and adjusting the error until zero error is reached, and saving the learning value.

[0012] Furthermore, the identifying the boundary of each label specifically includes:

[0013] The light intensity change detected by the reflective sensor is converted into an electrical signal, the electrical signal is filtered and normalized, and then the boundary of each label is identified in the image data through an edge detection algorithm such as the Canny edge detector;

[0014] Specifically, the output electrical signal of the reflective sensor is expressed as: V(t)=a·I(t), where V(t) is the electrical signal, I(t) is the light intensity, and a is the gain coefficient of the sensor.

[0015] Furthermore, the image source file is converted into a raster dot matrix image required by the printer using a RIP tool, and label data is imported for printing, specifically including:

[0016] Start the online tool on the computer to import pre-prepared label data or data generated by other RIP tools, and then use the RIP tool to convert the image source file into a raster dot matrix image that the printer can recognize. In the online tool, the operator needs to specify the total number of labels to be printed, and the number of rows and columns will be automatically calculated and adjusted to optimize the print layout; then the operator triggers the print command to start the print job process. The system interface will dynamically display the print progress bar, record the number of printed labels in real time, and generate a log.

[0017] Furthermore, the surface data of the label paper consumables is collected in real time during the printing operation, and the error is calculated by comparing the real-time data with the learning value, and the X-direction and Y-direction servo motors are adjusted in real time, specifically including:

[0018] The reflective sensor collects the surface data of the label paper consumables in real time, digitizes it through the 12-bit ADC, converts it into precise pulse data and sends it to the 32-bit MCU, where the MCU compares the real-time data with the learning value stored in SPIFLASH and calculates the error value. Based on the calculated error value, the MCU dynamically adjusts the temporary learning value stored in SRAM and transmits the updated value to the printing control system through the RS485 bus to fine-tune the position of the X- and Y-direction servo motors in real time to ensure accurate alignment of the inkjet printing, starting from the second PASS of the printing job and continuing until the job is completed.

[0019] Furthermore, the error value is calculated based on the error after the analog signal collected by the reflective sensor is converted by ADC and compared with the stored learning value, and after being adjusted by the proportional coefficient, it is added to the old position of the servo motor to obtain the new position. The new position of the servo motor is expressed as: P new =P old +c(ADC(D analog )-L), where P old is the old position of the servo motor, ADC (D analog ) is a digital signal converted by a 12-bit ADC, i.e., an analog signal collected by a reflective sensor D analog After quantization, L is the learning value stored in SPIFLASH, and c is the proportional coefficient, which is used to adjust the influence of the error on the position adjustment of the servo motor.

[0020] Furthermore, the real-time collection of surface data of label paper consumables by the reflective sensor specifically includes:

[0021] Preprocess the collected surface data, determine the state variables of the position and speed of the label paper consumables, and set the initial state estimate and initial covariance matrix of the Kalman filter;

[0022] Predict the system state based on the state transition model in the Kalman filter, and use the observation model and actual observation data to update the predicted state and covariance to obtain accurate state estimation;

[0023] Furthermore, the system state prediction is expressed as: in is the prior state estimate at time step k, F k is the state transition matrix, which describes how the state evolves over time, is the posterior state estimate at time step k-1, B k is the control matrix, and the control input u k Associated with state transfer, w k is the process noise;

[0024] The covariance prediction is expressed as: Among them, P k|k-1 is the prior state covariance estimate at time step k, P k-1|k-1 is the posterior state covariance estimate at time step k-1, Q k is the covariance matrix of the process noise.

[0025] The beneficial effects of the present invention are:

[0026] The label boundary automatic recognition system based on reflective sensors effectively solves the problems existing in the traditional label paper printing method and significantly improves the printing efficiency and quality. Through the label boundary automatic learning module, the reflective sensor is used to learn and identify the physical characteristics of different label paper consumables, including material differences, surface flatness and typesetting errors, and these learning values ​​are automatically recorded and saved in SPIFLASH. In this way, the surface data of the label paper consumables can be collected in real time during the printing operation, and these real-time data can be compared with the learning value to calculate the error value. Then, the position of the X-direction and Y-direction servo motors can be adjusted in real time after adjusting the proportional coefficient to ensure the precise alignment of the inkjet landing point. This real-time automatic correction function starts from the second PASS of the printing operation and continues until the operation is completed, which significantly reduces the scrap rate caused by position deviation, saves ink consumption, reduces production costs, and conforms to the concept of green and environmentally friendly production.

[0027] Through the print job module, the RIP tool is used to convert the image source file into the raster dot matrix image required by the printer, and the label data is imported for printing. At the same time, the printing data is automatically recorded, the real-time progress bar is displayed, and the label quantity log is recorded. Data export is supported, which further improves the automation level and efficiency of the operation. The data recording and analysis module sends and records the printing data to the printing system in real time through RS485, which provides convenience for production statistics, and supports data export, enhancing the flexibility and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0029] Figure 1 This is a schematic diagram of the structure of the automatic label boundary recognition system based on reflective sensor provided in Example 1 of the present application. DETAILED DESCRIPTION

[0030] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0031] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0032] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0033] Example 1

[0034] See also Figure 1 , this embodiment provides a label boundary automatic recognition system based on a reflective sensor, which is used to solve the problems existing in the traditional label printing method, improve the efficiency and accuracy of label printing, and reduce resource consumption.

[0035] The label boundary automatic recognition system based on reflective sensor provided by the present invention comprises:

[0036] The label boundary automatic learning module uses a reflective sensor to learn the physical characteristics of different label paper consumables, including material differences, surface flatness, typesetting errors and other factors, to identify the boundary of each label, and automatically records and saves the learning value in SPIFLASH to ensure the accuracy of the inkjet landing point;

[0037] The print job module uses the RIP tool (Raster Image Processor) to convert the image source file into the raster dot matrix image required by the printer, and imports the label data for printing. It automatically records the print data, displays a real-time progress bar, and records the number of labels to generate a log, supporting data export;

[0038] The real-time automatic correction module collects the surface data of label paper consumables in real time during the printing job, calculates the error by comparing the real-time data with the learning value, and adjusts the X and Y servo motors in real time to ensure the accuracy of the inkjet landing point. The real-time automatic correction function starts from the second PASS of the printing job, and has 10 levels of sensitivity adjustment.

[0039] The data recording and analysis module sends and records printing data to the printing system in real time via RS485. The interface displays a real-time progress bar and records the number of labels generated. It is equipped with production statistics and supports data export.

[0040] The control and user interface module includes an operation panel, through which the X-direction and Y-direction learning is performed, a printing job is started, and the sensitivity of the real-time automatic correction module is adjusted. The operation panel displays the "array spacing" information of the X-direction and Y-direction, and provides the functions of error correction and saving the learning value.

[0041] Specifically, the highly sensitive reflective sensor can quickly identify the boundaries of label consumables in real time. The system is not limited to the consumables themselves and can be widely used in various types of label papers and printers; real-time automatic correction function: to maximize the accuracy of inkjet placement during the printing process, reduce consumables and ink consumption, and promote green and environmentally friendly production; through data recording and analysis, the system can send and record printing data to the printing system in real time via RS485, and support data export;

[0042] This embodiment uses boundary difference as the basis for identification, which is not limited to the situation of the consumables themselves. It can be widely used in various types of label papers and printers, has strong adaptability and is easy to integrate. During printing, due to the influence of factors such as the surface flatness of the consumables themselves, typesetting errors, base color block uniformity, label base thickness, and printing mechanism motion errors, the initially learned data or the data learned in the previous PASS deviates from the real-time data of the current sensor. This system makes full use of this real-time deviation, enables the real-time automatic correction function, and cooperates with the 10-level sensitivity adjustment to maximize the accuracy of the inkjet landing point during the printing process, ensure automated operations according to quality and quantity, reduce the consumption of label paper consumables, ink, etc., and promote green and environmentally friendly production.

[0043] Furthermore, before learning the material differences, surface flatness and typesetting errors of different label paper consumables through the reflective sensor, it includes: installing the label paper consumables into the printer, then starting the learning program through the operation panel, and then performing X-direction learning, driving the spray car to automatically move in the X direction and collect data of 6 label distances 3 times, comparing the collected array information with the actual consumables information, calculating the error, and adjusting it until the error is eliminated to zero, and then storing the learning value in SPIFLASH; according to the X-direction learning result, performing Y-direction learning, similarly collecting and comparing the array information, calculating and adjusting the error until zero error is reached, and saving the learning value.

[0044] Specifically, first install the label paper consumables to be printed on the printer; click the learning button in the operation panel to enter the X-direction learning, the inkjet machine automatically moves back and forth 6 label distances in the X direction for a total of 3 times by default (the number of learning labels and the number of times can be adjusted), and the returned X-direction array information is displayed in the X-direction "Array Spacing" column of the operation panel, check the X-direction array information of the actual label consumables, fill in the error = learning value - actual value in the corresponding "Error" column, click learning again, until the error = learning value - actual value = 0, click Save, and the learning value is automatically saved in SPIFLASH; return to the parent directory, and the learning can be completed in 2 to 3 times under normal circumstances;

[0045] Click the learning button in the operation panel to enter the Y-direction learning. Depending on the learning value in the X-direction, the X-direction servo motor drives the spray car to automatically move to the middle position of the first label in the X-direction of the consumables. Under the action of the Y-direction servo motor, the consumables automatically move back and forth in the Y direction for 6 label distances for a total of 3 times by default (the number and number of learning labels can be adjusted), and the returned Y-direction array information is displayed in the Y-direction "Array Spacing" column of the operation panel. Check the Y-direction array information of the actual label consumables, fill in the corresponding "Error" column with error = learning value - actual value, click learning again, until error = learning value - actual value = 0, click save, and the learning value is automatically saved in SPIFLASH; return to the parent directory, and learning can be completed in 2 to 3 times under normal circumstances.

[0046] Furthermore, the identifying the boundary of each label specifically includes:

[0047] The light intensity change detected by the reflective sensor is converted into an electrical signal, the electrical signal is filtered and normalized, and then the boundary of each label is identified in the image data through an edge detection algorithm such as the Canny edge detector;

[0048] Specifically, the output electrical signal of the reflective sensor is expressed as: V(t)=a·I(t), where V(t) is the electrical signal, I(t) is the light intensity, and a is the gain coefficient of the sensor.

[0049] Furthermore, the image source file is converted into a raster dot matrix image required by the printer using a RIP tool (Raster Image Processor), and the label data is imported for printing, specifically including:

[0050] Start the online tool on the computer to import pre-prepared label data or data generated by other RIP tools, and then use the RIP tool to convert the image source file into a raster dot matrix image that the printer can recognize. In the online tool, the operator needs to specify the total number of labels to be printed, and the number of rows and columns will be automatically calculated and adjusted to optimize the print layout; then the operator triggers the print command to start the print job process. The system interface will dynamically display the print progress bar, record the number of printed labels in real time, and generate a log.

[0051] Specifically, open the online tool on the computer, import the pre-prepared label data or label data generated by other RIP tools, fill in the number of labels to be printed in the job volume (the system automatically rounds up the number of rows and columns), click Start Printing, enter the print job process, the interface displays a real-time progress bar, and records the number of labels generated in the log, production statistics are prepared, and support data, setting parameters, etc. to be exported for use by other machines.

[0052] RIP tool: Raster Image Processor (Raster Image Processor), which means image processing software used for printing or copying. Its function is to convert the image source file into the raster dot matrix image required by the printer.

[0053] Furthermore, in the printing operation, the surface data of the label paper consumables is collected in real time, and the error is calculated by comparing the real-time data with the learning value, and the X-direction and Y-direction servo motors are adjusted in real time, which specifically includes: collecting the surface data of the label paper consumables in real time according to the reflective sensor, digitally quantizing it through the 12-bit ADC, converting it into precise pulse data and sending it to the 32-bit MCU, wherein the MCU compares the real-time data with the learning value stored in the SPIFLASH, calculates the error value, and according to the calculated error value, the MCU dynamically adjusts the temporary learning value stored in the SRAM, and transmits the updated value to the printing control system through the RS485 bus, so as to fine-tune the position of the X-direction and Y-direction servo motors in real time to ensure the precise alignment of the inkjet printing. This process starts from the second PASS of the printing operation and continues until the operation is completed.

[0054] Among them, SPIFLASH is a non-volatile memory that uses a serial peripheral interface (SPI) for communication. This type of memory is commonly used in embedded systems to store firmware, configuration parameters, calibration data, and other information that needs to remain unchanged after the system is powered off. Specifically, SPIFLASH is used to store the learning values ​​of parameters such as material differences, surface flatness, and typesetting errors of label paper consumables obtained through reflective sensor learning. These learning values ​​are read when the system starts or executes a print job, and are used to compare with the real-time collected data to calculate the error and adjust the position of the servo motor in real time to ensure accurate alignment of inkjet printing. SPIFLASH is due to its fast reading and writing capabilities, miniaturization, and low power consumption.

[0055] Specifically, due to the influence of factors such as the surface flatness of the consumables themselves, typesetting errors, base color block uniformity, label base thickness, and printing mechanism motion errors, there is a deviation between the initially learned data or the data learned in the previous PASS and the real-time data of the current sensor. If this is not processed and the pre-printing learning values ​​are continued to be used step by step, the printing quality will be acceptable in the initial stage, but as the printing operation continues, the deviation will become larger and larger, and the product will have to be treated as scrap.

[0056] This system fully considers the harmfulness of this error and starts the real-time automatic correction function at the beginning of the printing job. To be precise, the real-time automatic correction function starts from the second PASS of the printing job. Among them, PASS in the field of digital inkjet printing, the printing screen is divided into several small areas, and the number of times the unit area is covered by inkjet during printing is called the PASS number. Generally, for specific consumables, the higher the PASS number, the better the printing quality, but the speed is correspondingly reduced. Usually, printers have 4PASS, 6PASS and 8PASS.

[0057] The first PASS uses the learning value in SPIFLASH to determine the inkjet drop point. At the same time, the actual data returned by the reflective sensor in real time is digitally quantized by the 12-bit ADC, and the 12-bit precision pulse data is sent to the 32-bit MCU. The core component MCU will compare and calculate with the learning value in SPIFLASH, and call the learning function module to store the error = learning value - actual value in the SRAM unit of the MCU. At the same time, the refreshed temporary learning value is transmitted to the printing control system in real time through the RS485 bus, and the X-direction servo and Y-direction servo are fine-tuned during the printing of the second PASS, with the goal of making the inkjet drop point of the second PASS accurate.

[0058] The second PASS uses the temporary learning value of the first PASS to determine the inkjet point, and at the same time, the actual data returned by the reflective sensor in real time is digitally quantized by the 12-bit ADC, and the 12-bit precision pulse data is sent to the 32-bit MCU. The core component MCU compares and calculates with the first PASS learning value, and calls the learning function module to store the error = learning value - actual value in the MCU's SRAM unit. At the same time, the refreshed temporary learning value is transmitted to the printing control system in real time through the RS485 bus, and the X-direction servo and Y-direction servo are fine-tuned during the third PASS printing, with the goal of making the third PASS inkjet point accurate.

[0059] As the printing job proceeds, the learning module continuously refreshes the temporary learning value of the SRAM unit at each PASS, and the real-time correction module fine-tunes the X- and Y-direction servo motors at each PASS; this is repeated to maximize the accuracy of the inkjet landing point at each PASS during the printing process; the real-time correction function of this system is equipped with 10 levels of sensitivity adjustment, which can be used to observe the print quality in the early stage of printing and adjust it to a satisfactory effect level in real time, stop adjusting, and subsequent printing will maintain this effect until the entire printing task is completed, and automatically save the real-time correction sensitivity parameters in SPIFLASH. Ensure automated operations in quality and quantity to the greatest extent, reduce the consumption of label paper consumables, ink, etc., improve the cost-effectiveness of the machine, and promote green and environmentally friendly production. Specifically, there are 10 levels of sensitivity adjustment, which can be adjusted to a satisfactory effect level in real time according to the print quality, and automatically save the real-time correction sensitivity parameters in SPIFLASH.

[0060] Furthermore, the error value is calculated based on the error after the analog signal collected by the reflective sensor is converted by ADC and compared with the stored learning value, and after being adjusted by the proportional coefficient, it is added to the old position of the servo motor to obtain the new position. The new position of the servo motor is expressed as: P new =P old +c(ADC(D analog )-L), where P old is the old position of the servo motor, ADC (D analog ) is a digital signal converted by a 12-bit ADC, i.e., an analog signal collected by a reflective sensor D analog After quantization, L is the learning value stored in SPIFLASH, and c is the proportional coefficient, which is used to adjust the influence of the error on the position adjustment of the servo motor.

[0061] Specifically, through an advanced feedback control system, the new position of the servo motor is accurately calculated and adjusted to achieve high-precision alignment for inkjet printing. This process involves converting the analog signal collected by the reflective sensor into a digital signal through a 12-bit ADC, and then comparing it with the learning value stored in SPIFLASH to calculate the error. The calculated error value is adjusted by the proportional coefficient c to determine the degree of its impact on the servo motor position, and finally added to the old position of the servo motor to obtain the new position, ensuring the accuracy of the inkjet landing point in the printing job, thereby significantly improving printing quality and efficiency.

[0062] Furthermore, the real-time collection of surface data of label paper consumables by the reflective sensor specifically includes:

[0063] Preprocess the collected surface data, determine the state variables of the position and speed of the label paper consumables, and set the initial state estimate and initial covariance matrix of the Kalman filter;

[0064] Predict the system state based on the state transition model in the Kalman filter, and use the observation model and actual observation data to update the predicted state and covariance to obtain a more accurate state estimate;

[0065] Furthermore, the system state prediction is expressed as: in is the prior state estimate at time step k, F k is the state transition matrix, which describes how the state evolves over time, is the posterior state estimate at time step k-1, B k is the control matrix, and the control input u k Associated with state transfer, w k is the process noise;

[0066] The covariance prediction is expressed as: Among them, P k|k-1 is the prior state covariance estimate at time step k, P k-1|k-1 is the posterior state covariance estimate at time step k-1, Q k is the covariance matrix of the process noise.

[0067] Specifically, in the label boundary automatic recognition system based on reflective sensors, the process of real-time collection of label paper consumables surface data first involves preprocessing the raw data collected by the sensor to eliminate noise and interference, and then determining the key state variables, namely the position and speed of the label paper consumables. Next, set the initial state estimate for the Kalman filter, which is usually based on prior knowledge or assumptions at system startup, as well as the initial covariance matrix, which represents the quantification of the uncertainty of these initial estimates. These steps provide a starting point for the Kalman filter, enabling it to accurately track the dynamic changes of label paper consumables in subsequent prediction and update cycles by fusing the prediction model and real-time observation data, thereby providing accurate feedback for the system's automatic correction and ensuring high accuracy during the inkjet printing process.

[0068] Specifically, the label paper printing boundary automatic recognition system based on reflective sensors has greatly improved the efficiency and accuracy of label printing through high-precision boundary recognition and automatic correction functions. In the future, we will continue to optimize system performance and provide high-quality label printing solutions for more industries.

[0069] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A label boundary automatic recognition system based on a reflective sensor, characterized in that: It includes label boundary automatic learning module, printing operation module, real-time automatic correction module, data recording and analysis module and control and user interface module. The label boundary automatic learning module learns the physical characteristics of different label paper consumables through a reflective sensor, identifies the boundary of each label, and automatically records and saves the learning value in SPIFLASH; The printing job module converts the image source file into the raster dot matrix image required by the printer using the RIP tool, and imports the label data for printing, automatically records the printing data, displays a real-time progress bar, and records the number of labels to generate a log; The real-time automatic correction module collects the surface data of the label paper consumables in real time during the printing operation, calculates the error by comparing the real-time data with the learning value, and adjusts the X-direction and Y-direction servo motors in real time, and enters the real-time automatic correction from the second PASS of the printing operation; The data recording and analysis module sends and records the printing data to the printing system in real time via RS485, displays a real-time progress bar on the interface, and records the number of labels generated in the log; The control and user interface module includes an operation panel, which starts the printing job and adjusts the sensitivity of the real-time automatic correction module by learning the X and Y directions, wherein the operation panel displays the "array spacing" information of the X and Y directions.

2. The label boundary automatic recognition system based on reflective sensor according to claim 1, characterized in that: Before learning the material differences, surface flatness and typesetting errors of different label paper consumables through the reflective sensor, the method includes: installing the label paper consumables into the printer, then starting the learning program through the operation panel, and then performing X-direction learning, driving the spray car to automatically move in the X direction and collect data of 6 label distances 3 times, comparing the collected array information with the actual consumables information, calculating the error, and adjusting it until the error is eliminated to zero, and then storing the learning value in SPIFLASH; according to the X-direction learning result, performing Y-direction learning, similarly collecting and comparing the array information, calculating and adjusting the error, until the error reaches zero, and saving the learning value.

3. The label boundary automatic recognition system based on reflective sensor according to claim 1, characterized in that: The identifying the boundary of each label specifically includes: The light intensity change detected by the reflective sensor is converted into an electrical signal, the electrical signal is filtered and normalized, and then the boundary of each label is identified in the image data through an edge detection algorithm such as the Canny edge detector; Specifically, the output electrical signal of the reflective sensor is expressed as: V(t)=a·I(t), where V(t) is the electrical signal, I(t) is the light intensity, and a is the gain coefficient of the sensor.

4. The label boundary automatic recognition system based on reflective sensor according to claim 1, characterized in that: Use the RIP tool to convert the image source file into the raster dot matrix image required by the printer, and import the label data for printing, including: Start the online tool on the computer to import pre-prepared label data or data generated by other RIP tools, and then use the RIP tool to convert the image source file into a raster dot matrix image that the printer can recognize. In the online tool, the operator needs to specify the total number of labels to be printed, and the number of rows and columns will be automatically calculated and adjusted to optimize the print layout; then the operator triggers the print command to start the print job process. The system interface will dynamically display the print progress bar, record the number of printed labels in real time, and generate a log.

5. The label boundary automatic recognition system based on reflective sensor according to claim 1, characterized in that: Collect the surface data of label paper consumables in real time during the printing operation, calculate the error by comparing the real-time data with the learning value, and adjust the X-axis and Y-axis servo motors in real time, including: The reflective sensor collects the surface data of the label paper consumables in real time, digitizes it through the 12-bit ADC, converts it into precise pulse data and sends it to the 32-bit MCU, where the MCU compares the real-time data with the learning value stored in SPIFLASH and calculates the error value. Based on the calculated error value, the MCU dynamically adjusts the temporary learning value stored in SRAM and transmits the updated value to the printing control system through the RS485 bus to fine-tune the position of the X- and Y-direction servo motors in real time to ensure accurate alignment of the inkjet printing, starting from the second PASS of the printing job and continuing until the job is completed.

6. The label boundary automatic recognition system based on reflective sensor according to claim 5, characterized in that: The error value is calculated based on the error after the analog signal collected by the reflective sensor is converted by ADC and compared with the stored learning value. After being adjusted by the proportional coefficient, it is added to the old position of the servo motor to obtain the new position. The new position of the servo motor is expressed as: P new =P old +c(ADC(D analog )-L) where P old is the old position of the servo motor, ADC (D analog ) is a digital signal converted by a 12-bit ADC, i.e., an analog signal collected by a reflective sensor D analog After quantization, L is the learning value stored in SPIFLASH, and c is the proportional coefficient, which is used to adjust the influence of the error on the position adjustment of the servo motor.

7. The label boundary automatic recognition system based on reflective sensor according to claim 4, characterized in that: The real-time collection of surface data of label paper consumables based on the reflective sensor specifically includes: Preprocess the collected surface data, determine the state variables of the position and speed of the label paper consumables, and set the initial state estimate and initial covariance matrix of the Kalman filter; The system state is predicted according to the state transition model in the Kalman filter, and the predicted state and covariance are updated using the observation model and actual observation data to obtain accurate state estimation.

8. The label boundary automatic recognition system based on reflective sensor according to claim 7, characterized in that: The prediction of the system state is expressed as: in is the prior state estimate at time step k, F k is the state transition matrix, which describes how the state evolves over time, is the posterior state estimate at time step k-1, B k is the control matrix, and the control input u k Associated with state transfer, w k is the process noise; The covariance prediction is expressed as: Among them, P k|k-1 is the prior state covariance estimate at time step k, P k-1|k-1 is the posterior state covariance estimate at time step k-1, Q k is the covariance matrix of the process noise.

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