Food printing machine operation state monitoring method and system

Abstract

The invention relates to the technical field of data processing, and discloses a food printing machine operation state monitoring method and system. The method comprises the following steps: carrying out hierarchical data acquisition on the food printing machine, and monitoring process parameters such as temperature, pressure and speed and material characteristics; establishing a parameter correlation matrix, and obtaining working condition characteristics; adjusting parameters such as nozzle temperature and ink pressure in a linkage manner to form an optimization instruction; evaluating the process parameter coupling degree through fault diagnosis, and generating an equipment state report; and the printing quality is evaluated in real time, a parameter fingerprint database is established, and closed-loop monitoring is realized. According to the method, correlation analysis and linkage adjustment of the process parameters of the food printing machine are achieved, and the stability and accuracy of the printing process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a food stamping machine running state monitoring method and system. BACKGROUND

[0002] In the food stamping industry, food stamping machines are widely used for pattern printing on the surfaces of cakes, biscuits, chocolates and other foods. Existing food stamping machines mainly realize pattern printing by controlling process parameters such as nozzle temperature, ink pressure, feeding speed and printing height. These devices usually use single parameter monitoring mode to independently adjust and control each parameter. During the printing process, the printing task is completed by real-time detection of parameter changes and simple closed-loop control combined with the preset standard parameter range.

[0003] However, the existing technology has obvious deficiencies: due to the complex correlation between process parameters in the food stamping process, independent monitoring of single parameters cannot accurately reflect the mutual influence between parameters. For example, when the nozzle temperature changes, it will affect the ink viscosity, and then affect the ink pressure and printing quality, but the existing technology lacks a systematic analysis and processing mechanism for such correlation effects. At the same time, different food materials have great differences in requirements for printing parameters, and the existing technology lacks targeted parameter optimization scheme, resulting in unstable printing quality. In addition, there is a lack of data sharing and collaborative optimization mechanism between devices, resulting in low efficiency of process parameter optimization. SUMMARY

[0004] The present application provides a food stamping machine running state monitoring method and system for realizing the correlation analysis and linkage adjustment of food stamping machine process parameters, and improving the stability and accuracy of the printing process.

[0005] In a first aspect, the present application provides a food stamping machine running state monitoring method, which comprises: collecting process parameters, material characteristics and image quality of a food stamping machine in layers to generate a monitoring data stream; establishing a parameter correlation matrix through a temperature-pressure-speed correlation modeler according to the monitoring data stream to obtain a working condition characteristic value; based on the working condition characteristic value, linkage adjustment is performed on the nozzle temperature, ink pressure, feeding speed and printing height to form a parameter optimization instruction set; according to the parameter optimization instruction set, the process parameter coupling degree, material adaptability and system performance are evaluated through a fault diagnosis model to obtain a device health status report; based on the device health status report, real-time evaluation is performed on the pattern accuracy, color restoration degree and edge definition, a process parameter fingerprint library is established, and closed-loop monitoring of the running state is realized.

[0006] In a second aspect, the application provides a food printing machine operation state monitoring system, comprising: a collection module configured to collect process parameters, material characteristics and image quality of the food printing machine in layers to generate a monitoring data stream; a building module configured to build a parameter correlation matrix through a temperature-pressure-speed correlation modeler based on the monitoring data stream to obtain a working condition characteristic value; an adjusting module configured to link and adjust a nozzle temperature, an ink pressure, a feeding speed and a printing height based on the working condition characteristic value to form a parameter optimization instruction set; an evaluation module configured to evaluate a process parameter coupling degree, a material adaptability and a system performance through a fault diagnosis model based on the parameter optimization instruction set to obtain a device health state report; a monitoring module configured to evaluate a pattern accuracy, a color restoration degree and an edge definition in real time based on the device health state report, build a process parameter fingerprint library and realize closed-loop monitoring of the operation state.

[0007] In a third aspect, the application provides a computer device, comprising a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to make the computer device execute the food printing machine operation state monitoring method.

[0008] In a fourth aspect, the application provides a computer readable storage medium, the computer readable storage medium storing instructions, when the instructions are executed on a computer, making the computer execute the food printing machine operation state monitoring method.

[0009] The technical scheme provided in the application, by layering process parameters, material characteristics and image quality data, establishing a monitoring data stream, constructing a parameter correlation matrix through a temperature-pressure-velocity correlation modeler, the accurate description of the correlation between process parameters is realized, and the problem that single parameter monitoring cannot reflect parameter coupling effect is effectively solved; based on the working condition characteristic value, the temperature of the nozzle, the ink pressure, the feeding speed and the printing height are linked and adjusted, a cooperative control mechanism between parameters is established, and the accuracy of parameter adjustment is significantly improved; through the fault diagnosis model, the process parameter coupling degree, the material adaptability and the system performance are evaluated, the overall monitoring of the equipment running state is realized, and the fault early warning capability of the system is enhanced; based on the equipment health state report, the pattern accuracy, the color restoration degree and the edge definition are evaluated in real time, a process parameter fingerprint library is established, a closed-loop monitoring system is formed, and the stability of the printing quality is greatly improved. The temperature-pressure-velocity correlation modeling algorithm adopted in the application fully considers the complex correlation between parameters in the food printing process, and the accurate modeling of the multi-parameter coupling effect is realized by establishing a parameter correlation matrix. The application of the algorithm enables the system to accurately capture the influence law of parameter change on printing quality, and provides reliable data support for parameter optimization. At the same time, the introduction of the fault diagnosis model realizes the intelligent identification and early warning of abnormal working conditions, and improves the reliability and maintenance efficiency of the system. In addition, the establishment of the process parameter fingerprint library provides a data basis for the printing parameter optimization of different types of food, and significantly improves the adaptability and working efficiency of the system. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0011] Figure 1 An embodiment schematic diagram of the food printing machine running state monitoring method in the embodiment of the application; Figure 2 An embodiment schematic diagram of the food printing machine running state monitoring method in the embodiment of the application; Figure 3 An embodiment schematic diagram of the food printing machine running state monitoring system in the embodiment of the application; Figure 4 An embodiment schematic diagram of the food printing machine running state monitoring system in the embodiment of the application; DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a food printing machine running state monitoring method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0013] For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 One embodiment of the food printing machine running state monitoring method in the embodiment of the present application comprises the following steps. Step S101, collecting process parameters, material characteristics and image quality of the food printing machine in layers to generate a monitoring data stream; Step S102, establishing a parameter correlation matrix through a temperature-pressure-speed correlation modeler according to the monitoring data stream to obtain a working condition characteristic value; Step S103, based on the working condition characteristic value, carrying out linkage adjustment on the nozzle temperature, ink pressure, feeding speed and printing height to form a parameter optimization instruction set; Step S104, according to the parameter optimization instruction set, evaluating the process parameter coupling degree, material adaptability and system performance through a fault diagnosis model to obtain a device health status report; Step S105, based on the device health status report, carrying out real-time evaluation on the pattern accuracy, color restoration degree and edge definition, establishing a process parameter fingerprint library, and realizing closed-loop monitoring of the running state.

[0014] It can be understood that the execution subject of the present application can be a food printing machine running state monitoring system, and can also be a terminal or a server, and the specific execution subject is not limited herein. The embodiment of the present application takes the server as the execution subject for example.

[0015] Specifically, four process parameters, i.e., the temperature of the nozzle, the ink pressure, the feeding speed and the printing height, are collected at a high frequency. During the collection, the signals are processed in layers through different sampling frequencies. The process parameters are collected at a sampling frequency of 50 Hz to obtain the original data, and the data noise is eliminated through waveform smoothing processing to obtain the process state data. Then, according to the sampling time sequence of the process state data, three material characteristic parameters, i.e., the surface temperature of the food, the moisture content of the material and the surface flatness, are synchronously sampled at a sampling frequency of 10 Hz to generate the material parameter data. Based on the sampling time of the material parameter data, the quality parameters, i.e., the printing precision, the color restoration degree and the edge sharpness, are collected at a frequency of 5 Hz to obtain the quality control data. The three types of data are converted in range and normalized according to the respective parameter characteristics to form the standardized monitoring data stream. During the establishment of the parameter correlation matrix by the temperature-pressure-velocity correlation modeler, the temperature data in the monitoring data stream are divided according to the printing period, which is usually 10-30 seconds. The maximum fluctuation range of the temperature in each period is extracted to generate a temperature feature sequence. Corresponding to the time of each temperature feature point, the change trend of the pressure data, including the rising rate, the stable time and the falling rate, is extracted to form a pressure response sequence. The pressure response sequence is time-aligned with the speed data at the same time to generate a speed compensation sequence. The temperature-pressure coupling value is calculated by the fluctuation range of the temperature feature sequence and the change trend of the pressure response sequence, which reflects the influence degree of temperature change on pressure adjustment. The temperature-pressure coupling value is time-mapped with the instantaneous change amount of the speed compensation sequence to extract the three-dimensional correlation features of temperature-pressure-velocity, and a parameter correlation matrix is constructed. The matrix is decomposed to obtain the temperature-pressure correlation coefficient, the speed-pressure correlation coefficient and the speed-temperature correlation coefficient, and the working condition characteristic value is obtained by weighted combination.

[0016] The correlation coefficients in the working condition characteristic value are converted into specific control parameters. The temperature-pressure correlation coefficient is converted into a temperature response coefficient through interval mapping, which is used to determine the adjustment direction and amplitude of the nozzle temperature. The speed-pressure correlation coefficient is converted into a pressure change coefficient through threshold decomposition, which is used to guide the adjustment strategy of the ink pressure. The speed-temperature correlation coefficient is converted into a speed offset coefficient through normalization processing, which is used for speed control. The height correction coefficient is calculated based on the combination relationship of the three coefficients. The adjustment interval of the nozzle temperature is calculated according to the temperature response coefficient to generate a temperature initial instruction containing the change direction and amplitude. The compensation value of the ink pressure is calculated by combining the change amplitude of the temperature initial instruction with the pressure change coefficient to form a pressure initial instruction. The change interval of the feeding speed is determined according to the adjustment step of the pressure initial instruction and the speed offset coefficient to generate a speed initial instruction. The height compensation instruction is generated by compensating according to the influence of speed change on the printing height and combining the height correction coefficient. These instructions are combined in time sequence to form a parameter optimization instruction set.

[0017] Periodically sample each instruction in the parameter optimization instruction set, record the actual execution effect, and generate an instruction tracking sequence. By analyzing the instruction tracking sequence, extract the temperature-pressure fluctuation rate, pressure-velocity response rate, and velocity-height change rate, and construct the process parameter coupling degree evaluation standard. Based on this standard, calculate the deviation of the actual parameters from the expected parameters during the printing process to form the material adaptability index. Compare and analyze the material adaptability index with the historical printing parameters, draw the printing stability curve, and calculate the system performance score. Weight the fluctuation rate and response rate in the process parameter coupling degree evaluation standard to obtain the parameter coupling score. Grade the deviations in the material adaptability index to obtain the adaptability score. Weight the parameter coupling score, adaptability score, and system performance score to generate the equipment operation evaluation report and form the equipment health status report.

[0018] Time-series sample the score data in the equipment health status report to establish a quality evaluation benchmark value. Compare the actual printed pattern with the standard pattern pixel by pixel, calculate the coincidence degree, analyze the color difference through the standard color card, and detect the gradient of the pattern edge. These data together constitute the pattern quality feature vector. Based on the quality evaluation benchmark value, grade and weight the feature vector to comprehensively calculate the printing quality score. Establish a corresponding relationship between the printing quality score and the real-time process parameters to generate the process parameter feature group. These feature groups are classified and stored according to different printing material types, pattern types, and environmental parameters to form the process parameter fingerprint library. Through this fingerprint library, continuously feedback and compare the printing quality score to constantly optimize the printing parameters and form a closed-loop monitoring. For example, during the biscuit surface pattern printing process, the process parameter collection shows that the nozzle temperature fluctuates in the range of 28±2℃, the ink pressure is maintained between 400-450dpi, the feeding speed is stable at 15 meters / minute, and the printing height is maintained at 32mm. Through correlation modeling analysis, it is found that every 1℃ increase in temperature causes the ink pressure to decrease by about 10dpi, and every 1 meter / minute change in speed requires an adjustment of about 20dpi in pressure. Based on these relationships, generate parameter optimization instructions, when the temperature is detected to rise to 29.5℃, the system automatically adjusts the ink pressure to 385dpi, and simultaneously fine-tunes the feeding speed to 14.8 meters / minute, ensuring the printing quality. Through continuous monitoring and parameter optimization, the precision of the printed pattern is maintained above 95%, the color difference is controlled within 3 units, and the edge definition reaches 90%, forming a running state monitoring closed loop.

[0019] In the embodiment of the present application, by layering process parameters, material characteristics and image quality data, a monitoring data stream is established, a parameter correlation matrix is constructed by a temperature-pressure-velocity correlation modeler, the correlation between process parameters is accurately described, and the problem that single parameter monitoring cannot reflect parameter coupling effect is effectively solved; based on the working condition characteristic value, the temperature of the nozzle, the ink pressure, the feeding speed and the printing height are linked and adjusted, a cooperative control mechanism between parameters is established, and the accuracy of parameter adjustment is significantly improved; through the fault diagnosis model, the process parameter coupling degree, the material adaptability and the system performance are evaluated, the overall monitoring of the equipment running state is realized, and the fault early warning capability of the system is enhanced; based on the equipment health state report, the pattern accuracy, the color restoration degree and the edge definition are evaluated in real time, a process parameter fingerprint library is established, a closed-loop monitoring system is formed, and the stability of the printing quality is greatly improved. The temperature-pressure-velocity correlation modeling algorithm adopted in the present application fully considers the complex correlation between parameters in the food printing process, accurately models the multi-parameter coupling effect by establishing a parameter correlation matrix, and the application of the algorithm enables the system to accurately capture the influence law of parameter change on printing quality, providing reliable data support for parameter optimization. At the same time, the introduction of the fault diagnosis model realizes intelligent identification and early warning of abnormal working conditions, improves the reliability and maintenance efficiency of the system. In addition, the establishment of the process parameter fingerprint library provides a data basis for the printing parameter optimization of different types of food, and significantly improves the adaptability and working efficiency of the system.

[0020] In a specific embodiment, the process of step S101 can specifically include the following steps: (1) The nozzle temperature, ink pressure, feeding speed and printing height are subjected to acquisition frequency division, the process parameters of high-frequency signals are subjected to waveform smoothing processing, and process state data is generated; (2) According to the division period of the process state data, the food surface temperature, material moisture content and surface flatness are synchronously sampled to obtain material parameter data; (3) Based on the sampling time of the material parameter data, the printing accuracy, color restoration degree and edge sharpness are subjected to data acquisition to obtain quality control data; (4) The process state data, material parameter data and quality control data are subjected to range conversion according to parameter characteristics to obtain standard dimension data, and the standard dimension data is subjected to normalization calculation to generate a monitoring data stream.

[0021] Specifically, as Figure 2The process parameter, material characteristic and image quality of the food printing machine in the embodiment of the application are collected in layers, as shown in the flowchart. The sampling frequency is divided according to the change characteristics of different parameters, the nozzle temperature and ink pressure are sampled at a high frequency of 50 Hz, because these two parameters have the most direct and rapid impact on the printing quality; the feeding speed and printing height are sampled at a medium frequency of 25 Hz, because the changes of these two parameters are relatively slow. The high-frequency signals collected are subjected to waveform smoothing processing, mainly using the moving average method, and the specific method is to take the data of five consecutive sampling points to obtain an arithmetic average of the data points. For example, for the temperature values of five consecutive sampling points of the nozzle temperature at a certain time [28.5, 28.3, 28.7, 28.4, 28.6] ℃, the average calculation obtains the smoothed temperature value of 28.5 ℃ at this time. All process parameter data are processed in this way to generate process state data.

[0022] According to the division period of the process state data, the sampling time sequence is determined. The process state data forms a data point every 0.1 seconds as a reference time sequence, and the sampling of the material parameters is synchronized according to this reference time sequence, but the sampling frequency is reduced to 10 Hz. The food surface temperature is collected by infrared temperature measurement, the material moisture content is measured by a capacitive sensor, and the surface flatness is obtained by laser displacement sensor scanning. The sampling time of these three parameters strictly corresponds to the time stamp of the process state data, ensuring the time sequence consistency of the data. Each parameter records the corresponding process parameter value at the sampling time to form a correlation data group. For example, at a certain sampling time, the data groups [surface temperature: 25℃, corresponding nozzle temperature: 28.5℃], [moisture content: 15%, corresponding ink pressure: 400 dpi], [flatness: ±0.2mm, corresponding printing height: 32mm] and the like are recorded to form the material parameter data.

[0023] After obtaining the material parameter data, the printing quality is evaluated and collected according to the sampling time of the material parameter data. The printing precision, color restoration degree and edge sharpness are sampled at a low frequency of 5 Hz, because the evaluation of these parameters requires a certain image processing time. The printing precision is calculated by image recognition technology to calculate the coincidence degree of the actual printing pattern and the standard pattern, the color restoration degree is measured by a spectrophotometer to measure the color difference value, and the edge sharpness is obtained by image gradient analysis. The collection of each quality parameter forms a corresponding relationship with the previous process parameters and material parameters to constitute a quality control data chain.

[0024] The process state data is range converted to convert parameters of different dimensions to a unified numerical range. The range of the nozzle temperature is 20-35°C, which is converted to a standard range of 0-100; the ink pressure is converted from 0-600 dpi to 0-100; the feeding speed is converted from 0-30 m / min to 0-100; and the printing height is converted from 30-34 mm to 0-100. The material parameter data is also similarly converted, with a ±10°C deviation of the surface temperature being converted to 0-100, a ±20% deviation of the moisture content being converted to 0-100, and a ±2 mm deviation of the flatness being converted to 0-100. The quality control data is also converted to a range of 0-100. The standard dimension data obtained after conversion is subjected to normalization calculation to map all data to the [0, 1] interval to form a monitoring data stream.

[0025] Taking cake surface chocolate pattern printing as an example, 50 sampling points of the nozzle temperature within 1 second are recorded as process parameters, and the original data sequence contains slight fluctuations caused by environmental interference. After sliding average processing, a smooth temperature curve is obtained, which reflects the actual change trend of the nozzle temperature. The ink pressure data recorded at the same time shows that the pressure fluctuation range is between 375-425 dpi, and after smoothing processing, the response characteristics of the pressure change with the temperature are clearly shown. In the corresponding material parameter acquisition link, the cake surface state data is recorded every 0.1 second, and it is found that the surface temperature changes with a lag of about 0.5 seconds with the change of the nozzle temperature, and the change of the moisture content is obviously related to the ink pressure. The correlation data is subjected to standardization processing. The quality parameter acquisition is evaluated every 0.2 seconds, and the change of the sharpness of the pattern edge is focused on. Through corresponding analysis with the process parameters, the key factors affecting the printing quality are found. After range conversion and normalization processing, all parameters are mapped to a unified numerical space, which is convenient for comprehensive analysis and optimization control.

[0026] In an embodiment, the process of performing step S102 can specifically include the following steps: (1) dividing the temperature data in the monitoring data stream into temperature working condition segments according to the printing period, extracting the fluctuation range of each temperature working condition segment, and generating a temperature feature sequence; (2) time series cutting the pressure data corresponding to the time of the temperature feature sequence, extracting the pressure change trend, and forming a pressure response sequence; (3) time aligning the pressure response sequence with the speed data at the same time to generate a speed compensation sequence; (4) calculating a temperature-pressure coupling value from the fluctuation range in the temperature feature sequence and the change trend in the pressure response sequence, time series mapping the temperature-pressure coupling value with the instantaneous change amount of the speed compensation sequence, extracting a temperature-pressure-speed three-dimensional correlation feature, and constructing a parameter correlation matrix according to the three-dimensional correlation feature; (5) The parameter correlation matrix is divided into a temperature-pressure sub-matrix, a pressure-velocity sub-matrix and a velocity-temperature sub-matrix, the sum of the main diagonal elements of the temperature-pressure sub-matrix is calculated to obtain a temperature-pressure correlation coefficient, the pressure-velocity sub-matrix is diagonalized to obtain a pressure-velocity correlation coefficient, and the velocity-temperature sub-matrix is extracted to obtain a velocity-temperature correlation coefficient, and the temperature-pressure correlation coefficient, the pressure-velocity correlation coefficient and the velocity-temperature correlation coefficient are combined by weighting to obtain a working condition characteristic value.

[0027] Specifically, the temperature data in the monitoring data stream is divided into time periods according to a printing period, wherein the printing period refers to the time interval required for the food stamping machine to complete a complete pattern printing, usually 10-30 seconds, and the temperature sensor collects the temperature data of the nozzle at a frequency of 50Hz in each period, the fluctuation range is obtained by calculating the difference between the maximum value and the minimum value of the temperature in each period, and the fluctuation range data of a plurality of continuous periods are arranged in time sequence to form a temperature characteristic sequence, which reflects the change law of the temperature in different printing stages.

[0028] The time sequence division of the pressure data corresponding to the time of the temperature characteristic sequence refers to the time stamp position of each data point in the temperature characteristic sequence, the pressure value recorded by the ink pressure sensor at the same time is extracted, the pressure change amount between continuous time points is obtained by calculating the difference between the pressure values, and these change amounts are classified and marked according to the rising, stable and falling trends, the rising trend represents that the pressure increase rate is greater than 5dpi / s, the stable trend represents that the pressure change rate is within ±2dpi / s, and the falling trend represents that the pressure decrease rate is greater than 5dpi / s, and all trend marks are arranged in time sequence to form a pressure response sequence.

[0029] The time alignment of the pressure response sequence and the velocity data at the same time is realized by time stamp matching, the speed sensor records the feeding speed at a frequency of 25Hz, when there is a pressure change trend in a time point in the pressure response sequence, the system automatically finds the speed data corresponding to the time point, calculates the offset amount of the speed relative to the standard feeding speed, the offset amount is the difference between the actual speed and the target speed, and all time point speed offset amounts are arranged in time sequence to generate a speed compensation sequence, which records the response of the speed parameter to the pressure change.

[0030] The fluctuation range in the temperature feature sequence and the change trend in the pressure response sequence are used to calculate the temperature-pressure coupling value by a numerical mapping method. The specific calculation method is to multiply the temperature fluctuation range value by the quantized value of the pressure change trend. The upward trend is quantized as +1, the stable trend is quantized as 0, and the downward trend is quantized as -1. The temperature-pressure coupling value reflects the influence degree of temperature change on pressure regulation. The time sequence mapping of the temperature-pressure coupling value and the instantaneous change amount of the speed compensation sequence means that the temperature-pressure coupling value is associated with the corresponding speed offset amount at each time point. A three-dimensional coordinate system is constructed, with the temperature fluctuation range as the X-axis, the pressure change trend as the Y-axis, and the speed offset amount as the Z-axis. Each time point data forms a coordinate point in the three-dimensional space. The distribution mode of all coordinate points constitutes a temperature-pressure-speed three-dimensional correlation feature, which describes the mutual influence relationship among the three parameters.

[0031] The parameter correlation matrix is constructed according to the three-dimensional correlation feature by converting the numerical relationship of the three-dimensional coordinate points into a matrix form. The matrix is a 3x3 square matrix. The first row and the first column element represents the correlation degree between temperature and temperature, which is always 1. The first row and the second column element represents the correlation degree between temperature and pressure. The first row and the third column element represents the correlation degree between temperature and speed. The second row and the first column element represents the correlation degree between pressure and temperature. The second row and the second column element represents the correlation degree between pressure and pressure, which is always 1. The second row and the third column element represents the correlation degree between pressure and speed. The third row and the first column element represents the correlation degree between speed and temperature. The third row and the second column element represents the correlation degree between speed and pressure. The third row and the third column element represents the correlation degree between speed and speed, which is always 1. The numerical value of each element is obtained by calculating the correlation coefficient of the corresponding parameter in the three-dimensional space.

[0032] The parameter correlation matrix is divided into three sub-matrices by extracting the element combination at a specific position. The temperature-pressure sub-matrix contains the elements in the first row and the second column and the second row and the first column of the matrix. The pressure-speed sub-matrix contains the elements in the second row and the third column and the third row and the second column of the matrix. The speed-temperature sub-matrix contains the elements in the third row and the first column and the first row and the third column of the matrix. The temperature-pressure correlation coefficient is calculated by adding the numerical values of the diagonal positions of the temperature-pressure sub-matrix. The pressure-speed sub-matrix is diagonalized by converting the matrix into a diagonal matrix form through the eigenvalue decomposition algorithm, and the maximum eigenvalue is extracted as the speed-pressure correlation coefficient. The feature direction of the speed-temperature sub-matrix is calculated by calculating the principal eigenvector of the matrix, and the modulus of the vector is taken as the speed-temperature correlation coefficient. The three correlation coefficients are weighted and summed according to the weight proportions of 0.4, 0.3, and 0.3 to obtain the operating condition characteristic value.

[0033] In a specific embodiment, the process of performing step S103 can specifically include the following steps: (1) The temperature response coefficient is obtained by interval mapping the temperature-pressure correlation coefficient in the working condition characteristic value, the pressure change coefficient is obtained by threshold decomposition of the speed-pressure correlation coefficient, and the speed offset coefficient is obtained by normalization processing of the speed-temperature correlation coefficient. The height correction coefficient is calculated through the combination of the three coefficients, and the parameter adjustment reference library is constructed; (2) The adjustment interval of the nozzle temperature is calculated according to the temperature response coefficient, and the temperature initial instruction is generated. The temperature initial instruction contains the temperature change direction and the change amplitude; (3) The ink pressure compensation value is calculated based on the product of the change amplitude of the temperature initial instruction and the pressure change coefficient, and the pressure initial instruction is generated. The pressure initial instruction contains the pressure adjustment direction and the adjustment step; (4) The adjustment interval of the feeding speed is determined by matching the adjustment step of the pressure initial instruction with the speed offset coefficient, and the speed initial instruction is generated. The speed initial instruction contains the speed change interval and the change period; (5) The adjustment amount of the printing height is determined by compensation calculation according to the change interval of the speed initial instruction and the height correction coefficient, and the height compensation instruction is generated. The height compensation instruction contains the height adjustment amount and the adjustment timing; (6) The parameter optimization instruction set is generated by time sequence combination of the temperature initial instruction, the pressure initial instruction, the speed initial instruction and the height compensation instruction.

[0034] Specifically, the process of interval mapping the temperature-pressure correlation coefficient in the working condition characteristic value to obtain the temperature response coefficient is to map the numerical range of the correlation coefficient from the original negative one to positive one interval to the standard interval of zero to one. The specific mapping method is to add one to the temperature-pressure correlation coefficient and then divide by two. Interval mapping is a standardization processing method in mathematics to convert one numerical range to another numerical range. The mapped temperature response coefficient reflects the influence weight of the temperature parameter on the overall printing quality. At the same time, the speed-pressure correlation coefficient is threshold decomposed to obtain the pressure change coefficient. Threshold decomposition means that according to the preset critical value, continuous values are divided into different discrete levels. When the speed-pressure correlation coefficient is greater than zero point five, the pressure change coefficient is set to one. When the speed-pressure correlation coefficient is between negative zero point five and zero point five, the pressure change coefficient is set to zero. When the speed-pressure correlation coefficient is less than negative zero point five, the pressure change coefficient is set to negative one. The speed-temperature correlation coefficient is normalized to obtain the speed offset coefficient. Normalization is a standardization method that scales values to the range of zero to one. The speed offset coefficient is obtained by dividing the absolute value of the speed-temperature correlation coefficient by the maximum value of the absolute values of all correlation coefficients. The height correction coefficient is calculated by combining the three coefficients according to a specific mathematical relationship. The height correction coefficient is equal to the temperature response coefficient multiplied by zero point four, plus the pressure change coefficient multiplied by zero point three, plus the speed offset coefficient multiplied by zero point three. The parameter adjustment reference library is constructed by classifying and storing the four coefficients according to the parameter type, forming four data sets of temperature adjustment reference, pressure adjustment reference, speed adjustment reference, and height adjustment reference. Each reference contains control information such as adjustment range, adjustment step, and adjustment direction of the corresponding parameter.

[0035] The adjustment interval of the nozzle temperature is calculated according to the temperature response coefficient by multiplying the temperature response coefficient by the preset temperature adjustment range to obtain the specific temperature adjustment amplitude. When the temperature response coefficient is zero point six, the temperature adjustment amplitude is equal to zero point six multiplied by the standard adjustment range of five degrees Celsius to obtain three degrees Celsius. The temperature adjustment interval is determined as the range from the current temperature minus one point five degrees Celsius to the current temperature plus one point five degrees Celsius. The temperature initial instruction contains two key information: temperature change direction and change amplitude. The temperature change direction is determined by comparing the current temperature with the target temperature. When the current temperature is higher than the target temperature, the change direction is cooling. When the current temperature is lower than the target temperature, the change direction is heating. The change amplitude is equal to half of the adjustment interval, i.e. one point five degrees Celsius.

[0036] The ink pressure compensation value is calculated based on the product of the temperature initial instruction change amplitude and the pressure change coefficient, which is -0.5, indicating that the ink pressure needs to be reduced. The pressure compensation value is converted to a specific pressure adjustment amount by multiplying it by the standard pressure adjustment ratio. When the standard pressure adjustment ratio is 10 dpi per degree Celsius, the pressure adjustment amount is -15 dpi, which is obtained by multiplying -0.5 by 10. The pressure initial instruction contains the pressure adjustment direction and the adjustment step size. The pressure adjustment direction is determined based on the positive or negative of the pressure adjustment amount. A negative value indicates a decrease in pressure, while a positive value indicates an increase in pressure. The adjustment step size is equal to the absolute value of the pressure adjustment amount divided by the adjustment times. When the adjustment times are three, the adjustment step size is 5 divided by 3, which is 5 dpi per time.

[0037] The adjustment step size of the pressure initial instruction is multiplied by the speed offset coefficient to determine the adjustment interval of the feeding speed. The result is 2, which is obtained by multiplying 5 dpi per time by 0.4. The adjustment interval is calculated by converting the numerical value according to a fixed proportional relationship. The speed adjustment reference value is converted to a specific speed adjustment amount by multiplying it by the speed adjustment conversion factor. When the speed adjustment conversion factor is 0.1 meters per minute per unit, the speed adjustment amount is 0.2 meters per minute, which is obtained by multiplying 2 by 0.1. The feeding speed adjustment interval is determined to be the range from the current speed minus 0.1 meters per minute to the current speed plus 0.1 meters per minute. The speed initial instruction contains the speed change interval and the change period. The speed change interval is equal to the adjustment interval, which is 0.1 meters per minute. The change period is determined based on the time interval of the pressure adjustment. When the pressure adjustment needs to be completed in three seconds, the speed change period is set to three seconds.

[0038] The adjustment amount of the printing height is calculated based on the change interval of the speed initial instruction and the height correction coefficient. The result is 0.003, which is obtained by multiplying 0.1 meters per minute by 0.03. The height adjustment reference value is converted to a specific height adjustment amount by multiplying it by the height adjustment conversion factor. When the height adjustment conversion factor is 10 millimeters per unit, the height adjustment amount is 0.03 millimeters, which is obtained by multiplying 0.003 by 10. The height compensation instruction contains the height adjustment amount and the adjustment timing. The height adjustment amount is equal to the height adjustment amount, which is 0.03 millimeters. The adjustment timing is determined based on the adjustment sequence of other parameters. The height adjustment is arranged to be executed after the temperature adjustment is completed and before the pressure adjustment begins.

[0039] The time sequence combination of the temperature initial instruction, the pressure initial instruction, the speed initial instruction and the height compensation instruction generates the parameter optimization instruction set. The four independent control instructions are arranged and combined according to the execution time sequence. The time sequence combination follows the physical dependence relationship of parameter adjustment. The temperature adjustment is executed as the first step because the temperature change affects the ink viscosity and then affects the pressure adjustment effect. The height adjustment is executed as the second step to create suitable physical conditions for subsequent pressure and speed adjustment. The pressure adjustment is executed as the third step. The speed adjustment is executed as the fourth step. An appropriate delay time is set between each adjustment step to ensure that the adjustment effect of the previous step is stable before executing the next step.

[0040] In a specific embodiment, the process of executing step S104 can specifically include the following steps: (1) periodically sampling the temperature initial instruction, the pressure initial instruction, the speed initial instruction and the height compensation instruction in the parameter optimization instruction set to generate an instruction tracking sequence; (2) extracting the temperature-pressure fluctuation rate, the pressure-speed response rate and the speed-height change rate from the instruction tracking sequence to construct a process parameter coupling degree evaluation standard; (3) calculating the deviation of the temperature response coefficient, the pressure change coefficient and the speed offset coefficient in the printing process based on the process parameter coupling degree evaluation standard to form a material adaptability index; (4) comparing the material adaptability index with the historical printing parameters to obtain a printing stability curve, and calculating a system performance score through the printing stability curve; (5) weighting the temperature-pressure fluctuation rate, the pressure-speed response rate and the speed-height change rate in the process parameter coupling degree evaluation standard by weight coefficients of 0.4, 0.3 and 0.3 respectively to obtain a parameter coupling score; (6) performing threshold classification processing on the temperature response coefficient deviation, the pressure change coefficient deviation and the speed offset coefficient deviation in the material adaptability index to obtain an adaptability score, and weighting and fusing the mean value of the parameter coupling score and the adaptability score with the system performance score to generate a device operation evaluation report; (7) sorting and labeling the device operation evaluation report according to the time stamp to generate a device health status report.

[0041] Specifically, periodically sampling the temperature initial instruction, pressure initial instruction, speed initial instruction and height compensation instruction in the parameter optimization instruction set means recording the execution state of each instruction according to a fixed time interval. Periodic sampling is a standard method of acquiring signal values at a constant time interval in data acquisition technology. The sampling frequency is set to ten hertz, that is, ten data are collected per second. Four key information of the target value, actual execution value, execution time and completion state of the instruction are recorded each time the sampling is performed. The sampling data of the temperature initial instruction includes the target temperature change, the actual temperature change, the temperature adjustment start time and the temperature adjustment completion time. The sampling data of the pressure initial instruction includes the target pressure adjustment, the actual pressure adjustment, the pressure adjustment direction and the pressure adjustment step execution. The sampling data of the speed initial instruction includes the target speed change interval, the actual speed change interval, the speed change period and the speed adjustment completion degree. The sampling data of the height compensation instruction includes the target height adjustment, the actual height adjustment, the adjustment time sequence execution and the height adjustment precision. All the sampling data are arranged in chronological order to form an instruction tracking sequence. The instruction tracking sequence is time sequence data recording the change trajectory of each parameter in the instruction execution process.

[0042] The process of extracting the temperature-pressure fluctuation rate, pressure-speed response rate and speed-height change rate from the instruction tracking sequence is a feature extraction and correlation analysis of time sequence data. The temperature-pressure fluctuation rate is an index for measuring the degree of synchronization between temperature change and pressure change. The calculation method is to divide the product of temperature change and pressure change by the square of the time interval. When temperature rise leads to pressure drop, the fluctuation rate is negative. When temperature drop leads to pressure rise, the fluctuation rate is also negative. When the change direction of temperature and pressure is consistent, the fluctuation rate is positive. The pressure-speed response rate is a quantitative index describing the response degree of pressure adjustment to speed adjustment. The calculation method is to multiply the ratio of pressure adjustment to speed adjustment by the inverse of response time. The response time is the time interval between the completion of pressure adjustment and the start of speed adjustment. The speed-height change rate is an evaluation index reflecting the coordination degree between speed adjustment and height adjustment. The calculation method is to divide the ratio of speed change amplitude to height adjustment amplitude by the respective adjustment time. The construction of the process parameter coupling degree evaluation standard is to combine the three change rate data according to a specific mathematical relationship. The coupling degree evaluation standard includes a comprehensive evaluation system of three dimensions of mutual influence strength between parameters, response time delay and adjustment precision deviation.

[0043] The deviation calculation of the temperature response coefficient, the pressure change coefficient and the speed offset coefficient in the printing process based on the process parameter coupling degree evaluation standard is to compare the actual measured coefficient value with the standard reference value. The deviation calculation is a calculation method in statistics for measuring the difference between the actual value and the expected value. The temperature response coefficient deviation is equal to the actual temperature response coefficient minus the standard temperature response coefficient. The pressure change coefficient deviation is equal to the actual pressure change coefficient minus the standard pressure change coefficient. The speed offset coefficient deviation is equal to the actual speed offset coefficient minus the standard speed offset coefficient. The standard reference value is derived from the most optimal parameter combination in the historical printing data. The material adaptability index is formed by weighted average calculation of the three deviation values according to the material type. The material adaptability index reflects the sensitivity and adaptability of the current printing material to the change of the process parameters. The weighted coefficients of different materials are determined according to the physical properties of the materials. The temperature sensitivity of hard materials is lower, so the temperature deviation weight is smaller. The pressure sensitivity of soft materials is higher, so the pressure deviation weight is larger.

[0044] The comparison of the material adaptability index with the historical printing parameters is realized through database query and statistical analysis. The historical printing parameters refer to the process parameter data and the corresponding printing quality evaluation results recorded by the equipment in the past printing tasks. The comparison process includes three links of data matching, trend analysis and abnormality detection. The data matching is to filter similar printing scenes from the historical database according to factors such as material type, pattern complexity and environmental conditions. The trend analysis is to find out the regularity characteristics of parameter changes by time series analysis of the matched historical data. The abnormality detection is to identify the abnormal values deviating from the normal range in the current material adaptability index. The printing stability curve is obtained by drawing the trajectory of the material adaptability index with time into a curve graph. The horizontal axis of the printing stability curve represents time, and the vertical axis represents the material adaptability index value. The smoothness of the curve reflects the stability of the printing process, and the fluctuation amplitude of the curve reflects the accuracy of parameter adjustment. The system performance score calculated through the printing stability curve is a quantitative evaluation of the statistical characteristics of the curve. The system performance score is equal to the curve smoothness multiplied by 0.5 plus the curve convergence multiplied by 0.3 plus the curve consistency multiplied by 0.2. The curve smoothness is obtained by calculating the variance between adjacent data points. The curve convergence is obtained by analyzing whether the curve tends to a stable value. The curve consistency is obtained by comparing the similarity between the curve and the standard curve.

[0045] The weight coefficients of 0.4, 0.3 and 0.3 are respectively assigned to the temperature-pressure fluctuation rate, the pressure-velocity response rate and the velocity-height change rate in the process parameter coupling evaluation standard for weighted calculation, which is determined according to the influence degree of each parameter on the printing quality. The weighted calculation is a calculation method for weighted average of multiple values according to the importance in statistics. The parameter coupling score is equal to the temperature-pressure fluctuation rate multiplied by 0.4 plus the pressure-velocity response rate multiplied by 0.3 plus the velocity-height change rate multiplied by 0.3. The determination of the weight coefficients is based on that the influence of temperature change on the printing quality is the most significant, and therefore the weight is the highest. The influences of pressure and velocity are equivalent, and therefore the weights are equal. The parameter coupling score is a comprehensive quantitative evaluation of the coordination degree between the process parameters.

[0046] The threshold classification processing of the temperature response coefficient deviation, the pressure change coefficient deviation and the velocity offset coefficient deviation in the material adaptability index is to divide the continuous deviation values into different level intervals according to the preset critical value. The threshold classification processing is a standard method for converting continuous variables into discrete variables in data analysis. When the absolute value of the deviation is less than 0.1, it is rated as excellent. When the absolute value of the deviation is between 0.1 and 0.2, it is rated as good. When the absolute value of the deviation is between 0.2 and 0.3, it is rated as qualified. When the absolute value of the deviation is greater than 0.3, it is rated as unqualified. The adaptability score is obtained by converting the ratings of the three deviations into numerical values and calculating the weighted average. The excellent rating corresponds to a value of 1, the good rating corresponds to a value of 0.8, the qualified rating corresponds to a value of 0.6, and the unqualified rating corresponds to a value of 0.4. The weighted fusion of the mean values of the parameter coupling score and the adaptability score with the system performance score is calculated by weighted average according to the ratio of 3:3:4. The generation of the equipment operation evaluation report is to integrate all the score data and analysis results into a structured document format.

[0047] The arrangement and labeling of the equipment operation evaluation report according to the time stamp is to add accurate time information to each data record. The time stamp is a standard format for recording the time of an event in a computer system, which contains complete time information including year, month, day, hour, minute, second and millisecond. The arrangement process sorts all the data in chronological order. The labeling process adds corresponding time labels and event type identifiers to each data item. The generation of the equipment health status report is to classify and summarize the arranged and labeled data according to the framework of the equipment operation state evaluation. The equipment health status report includes five main parts: parameter adjustment execution, process parameter coupling analysis, material adaptability evaluation, system performance evaluation and abnormal event record.

[0048] In a specific embodiment, the process of performing step S105 can specifically include the following steps: (1) Time series sampling of the parameter coupling score, the adaptability score and the system performance score in the equipment health status report to generate quality evaluation benchmark values; (2) The printed pattern is compared with the standard pattern at the pixel level to extract the coincidence value, the color difference is calculated through the standard color card to extract the color difference value, the gradient analysis is performed on the pattern edge to extract the sharpness value, and the pattern quality feature vector is generated; (3) The pattern quality feature vector is graded and weighted based on the quality evaluation benchmark value, the pattern precision score, the color restoration degree score and the edge definition score are weighted and calculated according to the proportion of 3:4:3 to obtain the printing quality score; (4) The mapping relationship between the printing quality score and the corresponding nozzle temperature, ink pressure, feeding speed and printing height is established to generate the process parameter feature group; (5) The process parameter feature group is classified and stored according to the printing material type, pattern type and environmental parameters to construct the process parameter fingerprint library; (6) The printing quality score is compared and fed back through the process parameter fingerprint library to generate the parameter optimization signal and form the closed-loop monitoring of the running state.

[0049] Specifically, the parameter coupling score, the adaptability score and the system performance score are time-series sampled at a frequency of 5Hz, and the change trend of each score in a continuous time period is recorded. Through the mean, variance and change rate analysis of these score data, the quality evaluation benchmark value is generated. In the process of printing chocolate patterns on the surface of the cake, the parameter coupling score gradually stabilized at 0.92 from the initial 0.88, the adaptability score increased from 0.90 to 0.94, and the system performance score increased from 0.86 to 0.90. The comprehensive analysis results of these data form the benchmark standard of quality evaluation. The actual printed pattern is compared with the standard pattern at the pixel level, and the coincidence degree is calculated through the image registration technology. The coincidence degree calculation considers the matching degree of the position offset, shape deformation and size ratio of the pattern. At the same time, the standard color card is used to evaluate the printed color, the difference between the actual printed color and the standard color card is measured by the spectrophotometer, and the CIE Lab color difference value is calculated. The sobel operator gradient analysis is performed on the pattern edge to extract the sharpness feature value of the edge. The three groups of data together constitute the pattern quality feature vector.

[0050] Based on the quality evaluation benchmark, each index in the pattern quality feature vector is graded and weighted. The pattern accuracy score is calculated from the coincidence degree data, the color restoration score is determined according to the color difference value, and the edge sharpness score is converted from the sharpness value. According to the proportion of 3:4:3, the comprehensive printing quality score is calculated by weighted calculation. This weight distribution fully considers the importance of color restoration in food printing, while taking into account the requirements of pattern accuracy and edge sharpness. A mapping relationship between the printing quality score and the process parameters is established. The nozzle temperature, ink pressure, feeding speed and printing height data at the time corresponding to each printing quality score are recorded, and the corresponding relationship between these parameters and the quality score is analyzed. For example, when the printing quality score reaches the optimal value, the corresponding optimal parameter combination is recorded: nozzle temperature 28.4°C, ink pressure 385 dpi, feeding speed 14.8 meters / minute, printing height 31.8 mm. These parameter combinations form a process parameter feature group, which serves as an optimization reference standard.

[0051] The classification storage of the process parameter feature group uses a multi-level index structure. According to the type of printing material, such as cakes, cookies, chocolates and other food characteristics, the parameter characteristics of different types of patterns are stored separately. Secondly, according to the pattern type classification, the parameters of different types of patterns such as text, graphics and photos are classified respectively. Considering the influence of environmental parameters, the optimal parameter combinations under different temperature and humidity conditions are stored separately. This classification method constitutes a process parameter fingerprint library.

[0052] Through the process parameter fingerprint library, closed-loop monitoring is realized. When a new printing task is executed, the corresponding parameter feature group is retrieved from the fingerprint library as a reference standard according to the current material type, pattern type and environmental conditions. The real-time printing quality score is compared with the reference standard, and if a deviation is found, the corresponding parameter tuning signal is generated. For example, when printing a logo pattern on the surface of a cookie, if the printing quality score is found to be lower than the reference standard, by analyzing the difference between the current process parameters and the optimal parameters in the fingerprint library, the adjustment instruction is generated: adjust the nozzle temperature from 29°C to 28.4°C, reduce the ink pressure from 400 dpi to 385 dpi, reduce the feeding speed from 15 meters / minute to 14.8 meters / minute, and adjust the printing height from 32 mm to 31.8 mm. Through this continuous parameter optimization, the stability of the printing quality is maintained.

[0053] In a specific embodiment, the food printing machine running state monitoring method further comprises the following steps: (1) Establish a parameter index in the process parameter feature group in the process parameter fingerprint library according to the food type and the printing difficulty, and generate a parameter retrieval directory; (2) Perform time series analysis on the printing quality scores of the same food type in the parameter retrieval directory, extract the quality fluctuation rule, and form a quality trend record; (3) Periodically clean up the process parameter fingerprint library based on the quality trend record, eliminate low-quality parameter combinations, and establish a simplified parameter library; (4) Backup and store the simplified parameter library according to the printing batch, generate historical parameter snapshots, and periodically update the historical parameter snapshots to maintain the timeliness of the process parameter fingerprint library; (5) Compare the process parameter fingerprints of multiple food printing machines, extract common parameter features, and establish a parameter sharing pool; (6) Calculate the similarity of the process parameter feature groups in the parameter sharing pool, generate a parameter matching degree index, and cross-validate the printing parameters of different machines based on the parameter matching degree index to form a multi-machine collaborative optimization strategy.

[0054] Specifically, the process parameter fingerprint library is systematically organized. The process parameter feature groups are indexed according to food types (such as cakes, cookies, chocolates, etc.) and printing difficulty levels (basic patterns, complex patterns, fine patterns, etc.). The index structure contains standard parameter combinations for each food under different printing difficulties, including optimal nozzle temperature, ink pressure, feeding speed, and printing height. These parameter combinations form a parameter retrieval directory, facilitating quick positioning of appropriate printing parameters.

[0055] Based on the parameter retrieval directory, time series analysis is performed on the printing quality scores for each food type. Through statistical analysis of quality scores in continuous printing processes, regular features of quality fluctuations are extracted. For example, in cake surface pattern printing, it is found through analysis that the higher the temperature, the greater the fluctuation amplitude of the printing quality score. This regularity is recorded in the quality trend record. The quality trend record contains the influence of parameter fluctuations on printing quality, providing a basis for parameter optimization. Based on the quality trend record, the process parameter fingerprint library is periodically cleaned and optimized. By setting a quality score threshold, parameter combinations below the threshold are removed from the fingerprint library. During the removal process, the frequency of use and quality stability of parameter combinations are considered, and those that are both commonly used and stable are retained to form a simplified parameter library. The simplified parameter library has higher reference value. Data backup management is implemented on the simplified parameter library, and the parameter library is stored according to the printing batch to generate historical parameter snapshots. Each snapshot contains all high-quality parameter combinations in the printing process of that batch. New printing data is periodically compared with historical parameter snapshots to update the optimal parameter combinations in the parameter library, ensuring that the parameter library always contains the latest process parameter data. Multi-machine collaborative optimization of food printing machines is an important means to improve overall printing quality. Collect process parameter fingerprint library data from multiple machines, analyze parameters, and extract common high-quality parameter features. The features reflect the common rules of different machines in the same printing task, forming a parameter sharing pool. The data in the parameter sharing pool has stronger universality and reference value.

[0056] The similarity of the process parameter feature groups in the parameter sharing pool is analyzed, and the matching degree between different parameter combinations is calculated. The matching degree calculation considers the closeness of parameter values and the similarity of quality scores to generate a parameter matching degree index. Based on this index, the printing parameters of different devices are cross-verified. When the printing parameters of a device produce good results, its parameter combination will be recommended to other devices for verification, and in this way, multi-machine collaborative optimization is achieved.

[0057] The food printing machine running state monitoring method in the embodiments of the present application is described above, and the food printing machine running state monitoring system in the embodiments of the present application is described below. Please refer to Figure 3 An embodiment of the food printing machine running state monitoring system in the embodiments of the present application includes: The acquisition module is configured to perform layered acquisition of process parameters, material characteristics, and image quality of the food printing machine, and generate a monitoring data stream. The establishment module is configured to establish a parameter correlation matrix through a temperature-pressure-speed correlation modeler based on the monitoring data stream, and obtain a working condition characteristic value. The adjustment module is configured to perform linkage adjustment of the nozzle temperature, ink pressure, feeding speed, and printing height based on the working condition characteristic value, and form a parameter optimization instruction set. The evaluation module is configured to evaluate the process parameter coupling degree, material adaptability, and system performance through a fault diagnosis model based on the parameter optimization instruction set, and obtain a device health status report. The monitoring module is configured to perform real-time evaluation of the pattern accuracy, color restoration degree, and edge definition based on the device health status report, establish a process parameter fingerprint library, and realize closed-loop monitoring of the running state.

[0058] Through the cooperation of the above-mentioned various components, through the hierarchical collection of process parameters, material characteristics and image quality data, a monitoring data stream is established, a parameter correlation matrix is constructed through a temperature-pressure-velocity correlation modeler, the correlation between the process parameters is accurately described, and the problem that single parameter monitoring cannot reflect parameter coupling effect is effectively solved; based on the working condition characteristic value, the temperature of the nozzle, the ink pressure, the feeding speed and the printing height are linked and adjusted, a cooperative control mechanism between parameters is established, and the accuracy of parameter adjustment is significantly improved; through the fault diagnosis model, the process parameter coupling degree, the material adaptability and the system performance are evaluated, the overall monitoring of the equipment running state is realized, and the fault early warning ability of the system is enhanced; based on the equipment health state report, the pattern accuracy, the color restoration degree and the edge definition are evaluated in real time, a process parameter fingerprint library is established, a closed-loop monitoring system is formed, and the stability of the printing quality is greatly improved. The temperature-pressure-velocity correlation modeling algorithm adopted in the application fully considers the complex correlation between parameters in the food printing process, and the accurate modeling of the multi-parameter coupling effect is realized by establishing a parameter correlation matrix. The application of the algorithm enables the system to accurately capture the influence law of parameter change on printing quality, and provides reliable data support for parameter optimization. At the same time, the introduction of the fault diagnosis model realizes the intelligent identification and early warning of abnormal working conditions, improves the reliability and maintenance efficiency of the system. In addition, the establishment of the process parameter fingerprint library provides a data basis for the printing parameter optimization of different types of food, and significantly improves the adaptability and working efficiency of the system.

[0059] Reference Figure 4 In the embodiment of the application, a computer device is also provided, which can be a server, and the internal structure thereof can be as shown in Figure 4 The computer device comprises a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in the embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above method.

[0060] Those skilled in the art can understand Figure 4 that the structure shown in the embodiment is only a block diagram of part of the structure related to the application scheme, and does not constitute a limitation on the computer device to which the application scheme is applied.

[0061] An embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method described above. It can be understood that the computer readable storage medium in the embodiment can be a volatile or non-volatile computer readable storage medium.

[0062] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium provided by the present application and used in the embodiments can include non-volatile and / or volatile memory. The non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and memory bus dynamic RAM (MDRAM), etc.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0064] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0065] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of monitoring the operating state of a food stamping machine, characterized in that, The food stamping machine operation state monitoring method comprises: The process parameters, material characteristics and image quality of the food stamping machine are collected in layers to generate a monitoring data stream; According to the monitoring data stream, a parameter correlation matrix is established by a temperature-pressure-speed correlation modeler to obtain a working condition characteristic value; Based on the working condition characteristic value, the temperature of the nozzle, the ink pressure, the feeding speed and the printing height are linked and adjusted to form a parameter optimization instruction set; According to the parameter optimization instruction set, the process parameter coupling degree, material adaptability and system performance are evaluated by a fault diagnosis model to obtain a device health status report; Based on the device health status report, the pattern accuracy, color restoration degree and edge definition are evaluated in real time to establish a process parameter fingerprint library, realizing closed-loop monitoring of the operation state.

2. The method of claim 1, wherein The process parameters, material characteristics and image quality of the food stamping machine are collected in layers to generate a monitoring data stream, comprising: The collection frequency of the nozzle temperature, ink pressure, feeding speed and printing height is divided, the process parameters of high-frequency signals are subjected to waveform smoothing processing to generate process state data; According to the division period of the process state data, the food surface temperature, material moisture content and surface flatness are synchronously sampled to obtain material parameter data; Based on the sampling time of the material parameter data, the printing accuracy, color restoration degree and edge sharpness are collected to obtain quality control data; The process state data, material parameter data and quality control data are subjected to range conversion according to parameter characteristics to obtain standard dimension data, and the standard dimension data are subjected to normalization calculation to generate the monitoring data stream.

3. The method of claim 1, wherein According to the monitoring data stream, a parameter correlation matrix is established by a temperature-pressure-speed correlation modeler to obtain a working condition characteristic value, comprising: The temperature data in the monitoring data stream is divided into temperature working condition segments according to the printing period, the fluctuation range of each temperature working condition segment is extracted to generate a temperature characteristic sequence; The pressure data at the corresponding time of the temperature characteristic sequence is subjected to time series division, the pressure change trend is extracted to form a pressure response sequence; The pressure response sequence is time-aligned with the speed data at the same time to generate a speed compensation sequence; The fluctuation range in the temperature characteristic sequence and the change trend in the pressure response sequence are calculated to obtain a temperature-pressure coupling value, the temperature-pressure coupling value and the instantaneous change amount of the speed compensation sequence are subjected to time series mapping to extract a temperature-pressure-speed three-dimensional correlation feature, and the three-dimensional correlation feature is used to construct the parameter correlation matrix; The parameter correlation matrix is divided into a temperature-pressure sub-matrix, a pressure-speed sub-matrix and a speed-temperature sub-matrix, the sum of the main diagonal elements of the temperature-pressure sub-matrix is calculated to obtain a temperature-pressure correlation coefficient, the pressure-speed sub-matrix is subjected to diagonalization processing to obtain a speed-pressure correlation coefficient, and the speed-temperature sub-matrix is extracted to obtain a speed-temperature correlation coefficient, and the temperature-pressure correlation coefficient, the speed-pressure correlation coefficient and the speed-temperature correlation coefficient are combined by weighting to obtain the working condition characteristic value.

4. The method of claim 1, wherein The parameter optimization instruction set is formed by linkage adjustment of the nozzle temperature, ink pressure, feeding speed and printing height based on the working condition characteristic value, including: The temperature response coefficient is obtained by interval mapping of the temperature-pressure correlation coefficient in the working condition characteristic value, the pressure change coefficient is obtained by threshold decomposition of the speed-pressure correlation coefficient, the speed offset coefficient is obtained by normalization processing of the speed-temperature correlation coefficient, the height correction coefficient is calculated through the combination relationship of the three coefficients, and the parameter adjustment benchmark library is constructed; The adjustment interval of the nozzle temperature is calculated according to the temperature response coefficient, and a temperature initial instruction is generated, which contains the temperature change direction and the change amplitude; The ink pressure compensation value is calculated based on the product of the change amplitude of the temperature initial instruction and the pressure change coefficient, and a pressure initial instruction is generated, which contains the pressure adjustment direction and the adjustment step; The adjustment interval of the feeding speed is determined by matching operation of the adjustment step of the pressure initial instruction and the speed offset coefficient, and a speed initial instruction is generated, which contains the speed change interval and the change period; The adjustment amount of the printing height is determined by compensation calculation of the change interval of the speed initial instruction and the height correction coefficient, and a height compensation instruction is generated, which contains the height adjustment amount and the adjustment timing; The parameter optimization instruction set is generated by time sequence combination of the temperature initial instruction, the pressure initial instruction, the speed initial instruction and the height compensation instruction.

5. The method of claim 1, wherein The device health status report is obtained by evaluating the process parameter coupling degree, material adaptability and system performance through the fault diagnosis model according to the parameter optimization instruction set, including: The instruction tracking sequence is generated by periodic sampling of the temperature initial instruction, the pressure initial instruction, the speed initial instruction and the height compensation instruction in the parameter optimization instruction set; The process parameter coupling degree evaluation standard is constructed by extracting the temperature-pressure fluctuation rate, the pressure-speed response rate and the speed-height change rate through the instruction tracking sequence; The material adaptability index is formed by deviation calculation of the temperature response coefficient, the pressure change coefficient and the speed offset coefficient in the printing process based on the process parameter coupling degree evaluation standard; The printing stability curve is obtained by comparing the material adaptability index with the historical printing parameters, and the system performance score is calculated through the printing stability curve; The parameter coupling score is obtained by weighted calculation of the temperature-pressure fluctuation rate, the pressure-speed response rate and the speed-height change rate in the process parameter coupling degree evaluation standard by assigning weight coefficients of 0.4, 0.3 and 0.3 respectively; The adaptability score is obtained by threshold classification processing of the temperature response coefficient deviation, the pressure change coefficient deviation and the speed offset coefficient deviation in the material adaptability index, and the device operation evaluation report is generated by weighted fusion of the mean value of the parameter coupling score and the adaptability score with the system performance score; The device operation evaluation report is sorted and labeled according to the time stamp to generate the device health status report.

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8. 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color restoration and edge definition based on the equipment health state report, establishment of a process parameter fingerprint library and closed-loop monitoring of the running state.

9. A computer device, comprising: The food printing machine comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the food printing machine running state monitoring method in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The food printing machine comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the food printing machine running state monitoring method in any one of claims 1 to 7 when executing the computer program.

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