Printing production data real-time acquisition and analysis method

Abstract

The invention specifically relates to the technical field of big data analysis, and discloses a printing production data real-time acquisition and analysis method, which comprises the following steps: S1, multi-source data acquisition: acquiring multi-dimensional printing production data; s2, carrying out real-time edge processing: obtaining a multi-dimensional printing production data set, and carrying out real-time analysis; s3, data depth analysis: obtaining a printing production quality evaluation index, and predicting a printing equipment fault probability; s4, optimization strategy generation: making a printing production optimization strategy; s5, automatic control execution: parameters of the printing production equipment are automatically adjusted according to the equipment adjusting instruction; s6, executing result feedback; multi-dimensional printing production data is collected through the data collection terminal, data processing and analysis are performed through the edge computing node in combination with the cloud server, a multi-dimensional analysis model and a prediction model are constructed through the cloud server, and an equipment adjustment instruction is executed through the automatic execution terminal. And the product percent of pass and the production stability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and more specifically, to a method for real-time collection and analysis of printing production data. Background Art

[0002] With the development of industry, the global manufacturing industry has entered a new stage of development, shifting from a processing-oriented industry to a high-tech industry. With the development of society, consumers' demand for personalized printed products is increasing. Printing companies need to collect and analyze data in real time to quickly respond to market changes and achieve large-scale personalized customized production. The maturity of industrial Internet of Things, big data, and artificial intelligence technologies has made real-time data collection and analysis technically feasible. Existing printing data collection and analysis methods specifically include printing data collection, printing data preprocessing, data storage and transmission, data analysis and modeling, and decision support and feedback. Through full-chain data collection, standardized data processing, data analysis, and closed-loop decision-making, real-time acquisition of printing data is achieved, which helps companies improve efficiency, optimize quality, and reduce printing costs.

[0003] However, in actual use, it still has some shortcomings. First, the data collection dimension is single. The existing printing data collection and analysis methods mainly focus on the basic operating parameters and production progress parameters of printing equipment, but the collection of implicit key data is insufficient, making it difficult to accurately locate the root cause and increasing quality control costs. Second, real-time analysis capabilities are insufficient. Existing printing data collection and analysis methods mostly use traditional batch processing models, which cannot meet the requirements of real-time analysis. Furthermore, they lack an efficient flow framework deployment plan, making it difficult to quickly process and analyze large amounts of data. This results in an inability to promptly detect abnormalities in the printing process. Third, data analysis capabilities are limited. The data analysis of existing printing data collection and analysis methods only stays at the statistical analysis level, lacking the ability to deeply mine and model data, making it difficult to achieve scientific decision-making and production optimization. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a method for real-time collection and analysis of printing production data, which collects multi-dimensional printing production data through a data collection terminal, performs data processing and analysis through edge computing nodes combined with cloud servers, and constructs multi-dimensional analysis models and prediction models through cloud servers, effectively solving the problems of single data collection dimension, insufficient real-time analysis capability, and limited data analysis capability raised in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for real-time collection and analysis of printing production data, comprising a data collection terminal, an edge computing node, a cloud server, an automatic control terminal, and a mobile interactive terminal, and the steps are as follows: S1: Multi-source data acquisition: The data acquisition terminal collects multi-dimensional printing production data in real time, including printing quality data, printing efficiency data, and equipment status data, and transmits the real-time collected data to the edge computing node; S2: Real-time edge processing: The edge computing node performs data cleaning and feature extraction on the multi-dimensional printing production data to obtain a multi-dimensional printing production data set, performs real-time analysis using preset thresholds, and transmits the multi-dimensional printing production data set to the cloud server; S3: Data in-depth analysis: The cloud server receives and stores the printing production data set, builds a printing production data analysis model, calculates the printing production quality evaluation index, and builds a prediction model to predict the probability of printing equipment failure; S4: Optimization strategy generation: The printing production quality is judged based on the printing production quality evaluation index. The printing production optimization strategy is formulated based on the failure probability of printing equipment and the printing production quality, and is sent to the edge computing node. S5: Automatic control execution: The edge computing node receives the printing production optimization strategy, generates equipment adjustment instructions, and sends them to the automatic control terminal, which automatically adjusts the parameters of the printing production equipment according to the equipment adjustment instructions; S6: Feedback on execution results: Use the data acquisition terminal to collect the adjusted multi-dimensional printing production data in real time, feed it back to the cloud server, and display the data of the printing production process in real time through the human-computer interaction terminal.

[0006] Technical effects and advantages of the present invention: The present invention uses a data acquisition terminal to collect multi-dimensional printing production data, including printing quality data, printing efficiency data, and equipment status data, forming a complete data link between equipment, materials, and quality. This breaks through the limitation of traditional methods that only collect basic equipment parameters, improves the accuracy of printing production quality control, avoids repeated trial and error caused by the lack of single-dimensional data, and improves product qualification rate. The present invention combines edge computing nodes with cloud servers for data processing and analysis, and adopts an edge computing node + cloud collaborative architecture. The edge computing nodes perform real-time cleaning and threshold judgment at the data source, and use the threshold judgment to trigger device protection instructions. The cloud device is responsible for complex model analysis, which meets the real-time monitoring requirements of high-speed printing production and improves production stability. The present invention constructs a multi-dimensional analysis model and a prediction model through a cloud server, calculates the printing production quality evaluation index through a weighted average model, and realizes the leap from basic statistics to in-depth prediction. By quantitatively evaluating production quality and equipment status, an accurate preventive maintenance plan is formulated, which reduces unplanned downtime and improves production efficiency and production automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 Schematic diagram of the method steps of the present invention.

[0008] Figure 2 It is a schematic diagram of the overall structure of the present invention.

[0009] Figure 3 Schematic diagram of the production status level determination steps of the present invention. DETAILED DESCRIPTION

[0010] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0011] As attached Figure 1 A method for real-time collection and analysis of printing production data is shown, including a data collection terminal, an edge computing node, a cloud server, an automatic control terminal and a mobile interactive terminal.

[0012] In a more specific application of the present invention, the data acquisition terminal is used to obtain physical data, image data, material information and equipment status in the printing production process in real time, covering multi-dimensional data, specifically including sensor type, visual detection type, label recognition type and device interface type acquisition equipment. The sensor type can be a vibration sensor and a pressure sensor, the visual detection type can be an industrial camera and a spectrometer, the label recognition type can be an RFID tag reader and a barcode scanner, and the device interface type can be a PLC data acquisition module. The edge computing node and the automatic control terminal are connected through industrial Ethernet to ensure the stability of data transmission.

[0013] Edge computing nodes are used to preprocess the raw data uploaded by data acquisition terminals, perform local real-time analysis, and unify the heterogeneous protocols of different data acquisition terminals. Specific equipment includes edge computing gateways and industrial-grade switches. Industrial-grade switches are used to achieve multi-terminal data aggregation and distribution. Edge computing nodes connect to cloud servers through 5G networks, transmit preprocessed structured data, and connect data acquisition terminals and automatic control terminals through industrial buses to form a localized data closed loop.

[0014] Cloud servers are used for data storage and management, in-depth data analysis and modeling, as well as remote monitoring and collaboration. Specifically, they include cloud server clusters and data middle platforms. The cloud server clusters are used to support elastic expansion of computing power, and the data middle platform is used to integrate production data and enterprise management data to build a unified database. The cloud servers are connected to mobile interactive terminals through 5G networks.

[0015] The automatic control terminal is used to automatically adjust printing production parameters according to the instructions transmitted by the edge computing node, including triggering equipment start and stop, parameter switching and alarm shutdown. Specifically, it includes a PLC controller, an industrial robot and an intelligent actuator. The PLC controller is used to receive control instructions from the edge computing node and drive the equipment to execute. The industrial robot is used to automatically complete material loading and unloading and defective product sorting operations. The intelligent actuator includes an electric control valve and a servo motor. The automatic control terminal is connected to the edge computing node through an industrial bus to receive real-time control signals.

[0016] Mobile interactive terminals are used for on-site data entry and query, early warning and collaborative response, and visual interaction. They specifically include industrial PDAs or tablets and smart wearable devices, which are connected to cloud servers through 5G networks. They support offline mode downloading of cached data and synchronization after connecting to the Internet.

[0017] For the connection methods of the above-mentioned data acquisition terminals, edge computing nodes, cloud servers, automatic control terminals and mobile interactive terminals, see Figure 2 .

[0018] The specific embodiment of the present invention comprises the following steps: S1: Multi-source data acquisition: The data acquisition terminal collects multi-dimensional printing production data in real time, including printing quality data, printing efficiency data, and equipment status data, and transmits the real-time collected data to the edge computing node; Furthermore, printing quality data includes color qualification rate, overprint error, number of paste-plate prints, total print quantity, dot gain rate and number of defective prints; printing efficiency data includes effective production time, number of prints produced and plate change efficiency; equipment status data includes vibration speed, maximum roller temperature, ambient temperature, roller length, operating time and number of failures.

[0019] In this embodiment, it should be specifically explained that the detection of color pass rate requires the installation of an intelligent industrial camera at the paper delivery end of the printing press, which captures the printed product image in real time, compares it with the standard color value to calculate the color difference, calculates the ratio of the qualified number to the total detection number, and obtains the color pass rate; the detection of overprint error requires the deployment of a laser displacement sensor at the paper delivery end to measure the physical offset of the overprint position; the detection of the number of sticky prints and defective prints requires the deployment of an industrial camera at the end of the printing press to continuously scan the prints, identify sticky areas and other defects, and count the number of unqualified prints; the measurement of dot gain rate requires the deployment of a high-resolution industrial camera at a sampling inspection station to capture the dot area, use software to analyze the degree of dot edge diffusion, and calculate the percentage difference between the actual dot area and the theoretical value.

[0020] Effective production time and operating time require real-time recording of equipment power-on, shutdown, and standby status through PLC, excluding non-production time such as plate change, failure, and manual intervention. The time of continuous operation and production of qualified products is counted as effective production time. The total operating time includes all start and stop cycles; the number of printed products produced requires the deployment of photoelectric counters at the end of the production line to monitor real-time production volume; the plate change efficiency requires scanning the plate roller RFID tag at the beginning of the plate change to trigger the timing, and stop the timing after the plate change is completed and calibrated. The ratio of standard time to actual plate change time is the plate change efficiency.

[0021] To detect vibration frequency, an acceleration sensor needs to be deployed on the bearing seat of the printing press drum to collect the vibration acceleration signal in real time and convert it into vibration speed; the drum temperature and ambient temperature are measured by deploying infrared temperature sensors on the drum surface and workshop walls, and the sensors transmit temperature data in real time; the number of faults needs to be detected by counting when the PLC receives abnormal signals from the equipment.

[0022] S2: Real-time edge processing: The edge computing node performs data cleaning and feature extraction on the multi-dimensional printing production data to obtain a multi-dimensional printing production data set, performs real-time analysis using preset thresholds, and transmits the multi-dimensional printing production data set to the cloud server; Furthermore, data cleaning specifically includes outlier removal, missing value processing and data standardization; feature extraction specifically includes time domain feature extraction, frequency domain feature extraction and image feature extraction; thresholds specifically include color deviation threshold, overprint error threshold, vibration speed threshold and roller temperature threshold. The edge computing node generates equipment adjustment instructions by comparing the real-time received color difference qualified rate, overprint error, vibration speed and roller temperature with the preset thresholds, and transmits them to the automatic control terminal to adjust the production parameters of the printing equipment in real time.

[0023] In this embodiment, it should be specifically explained that outlier elimination, missing value processing, and data standardization are to unify the physical quantities of different sensors into dimensionless values ​​and map the multi-dimensional printing production data to [0, 1] to facilitate subsequent analysis and processing.

[0024] Real-time analysis is performed using thresholds. For example, when the real-time monitored overprint error is greater than the threshold of 0.1mm, the equipment fine-tuning program is automatically triggered, and a correction instruction of increasing or decreasing by 0.5mm is sent to the printing press PLC through the OPC UA protocol. When the roller temperature is greater than 85 degrees Celsius, the edge computing node immediately reduces the printing speed and sends an emergency notification to the cloud server, which is then transmitted to the human-computer interaction terminal to notify relevant staff.

[0025] S3: Data in-depth analysis: The cloud server receives and stores the printing production data set, builds a printing production data analysis model, calculates the printing production quality evaluation index, and builds a prediction model to predict the probability of printing equipment failure; Furthermore, the calculation of the printing production quality evaluation index requires the printing quality evaluation index y1, the printing efficiency evaluation index y2, and the equipment status evaluation index y3. The printing quality evaluation index, the printing efficiency evaluation index, and the equipment status evaluation index are calculated using the formula The printing production quality evaluation index y is calculated, where d1, d2, and d3 represent the weight coefficients of the printing quality evaluation index, the printing efficiency evaluation index, and the equipment status evaluation index, respectively, and the total is 1.

[0026] Furthermore, the acquisition of the printing quality evaluation index requires setting a time window and obtaining the color qualification rate η of a single printed product in the printing production data set within the time window. 1i , overprint error d i and dot gain ratio r i , get the color pass rate mean η1 and the overprint error mean d, calculate the ratio of the difference between the single dot enlargement rate and the planned dot enlargement rate r to the planned dot enlargement rate using the formula Get the dot gain deviation r ai , and get the dot gain rate deviation mean r a , obtain the number of smeared prints n1, the total number of prints n, and the number of defective prints n2 in the printing production data set within the time window, and calculate the ratio of the number of smeared prints and the number of defective prints to the total number of prints to obtain the smeared print defect rate r h and the total defect rate r b , the color qualification rate, overprint error, dot gain deviation, smear printing defect rate and total defect rate are calculated through the formula: , Get the printing quality evaluation index y1, r标 Indicates the standard dot gain deviation, Δr max Indicates the maximum allowable dot gain deviation, d max Indicates the maximum permissible overprint error, r hmax Indicates the maximum allowable smear printing defect rate, r bmax represents the maximum allowable total defect rate, a1, a2, a3, a4, and a5 represent the corresponding weight coefficients, and the total is 1.

[0027] The acquisition of the printing efficiency evaluation index requires obtaining the effective production time t, the number of printed products N, and the plate change efficiency η2 in the printing production data set within the time window, and calculating the ratio of the effective production time to the maximum theoretical production time to obtain the time utilization rate η t , calculate the ratio of the number of printed products produced to the theoretical maximum number of printed products produced to obtain the performance utilization rate η N , using the time utilization, performance utilization and plate change efficiency formula: , Get the printing efficiency evaluation index y2, where η 2min and η 2max They represent the minimum and maximum values ​​of the plate changing efficiency respectively. b1, b2 and b3 represent the weight coefficients of time utilization, performance utilization and plate changing efficiency respectively, and their total is 1.

[0028] The acquisition of the equipment condition evaluation index requires the vibration speed V and the maximum roller temperature T in the printing production data set within the time window. 1m , ambient temperature T2, drum length l, operating time t1 and number of failures n3, using the maximum drum temperature, ambient temperature and drum length using the formula The temperature gradient D is calculated, and the ratio of the operating time to the number of failures is calculated to obtain the mean time between failures t w , using vibration velocity, temperature gradient and mean time between failures through the formula: , Get the equipment status evaluation index y3, where exp represents the exponential decay model, V 临界 represents the critical value of the vibration frequency, G1 is a piecewise linear model, t wmin and t wmax They represent the minimum and maximum values ​​of the mean time between failures, respectively. c1, c2 and c3 represent the weight coefficients of vibration velocity, temperature gradient and mean time between failures, respectively, and their total is 1.

[0029] In this embodiment, it should be specifically noted that all weight coefficients in the above calculation steps need to be obtained based on the analysis of a large amount of printing production data and are set by professional staff.

[0030] Piecewise linear models , where G 临界 and G 理想 represent the critical temperature gradient and ideal temperature gradient, respectively.

[0031] The construction of the prediction model requires dividing the multi-dimensional printing production dataset into training set, validation set and test set, performing undersampling and oversampling category imbalance processing, cross-validation, validation set parameter adjustment and test set evaluation. The evaluation indicators include classification indicators and time series prediction indicators, and hyperparameter tuning, ensemble learning and transfer learning are performed. The probability of printing equipment failure is obtained using the output of the deployed prediction model.

[0032] S4: Optimization strategy generation: The printing production quality is judged based on the printing production quality evaluation index. The printing production optimization strategy is formulated based on the failure probability of printing equipment and the printing production quality, and is sent to the edge computing node. Furthermore, the judgment of printing production quality requires the construction of a production quality assessment threshold. The printing production quality assessment index calculated in real time is compared with the production quality assessment threshold, and the production status levels are divided into the first level, the second level, the third level and the fourth level from high to low. The production optimization strategy is formulated by combining the production status level with the probability of printing equipment failure.

[0033] Furthermore, if Figure 3 As shown, the production status level judgment process steps are as follows: A1: When the printing production quality assessment index is greater than 0.8 and less than 1, it indicates that the production status belongs to the first level; This indicates that the production status is at an excellent level. By continuing to maintain the current process, we can explore room for efficiency improvement.

[0034] A2: When the printing production quality assessment index is greater than 0.6 and less than 0.8, it indicates that the production status belongs to the second level; This indicates that the production status is at a good level and requires local optimization, such as fine-tuning parameters or taking preventive measures.

[0035] A3: When the printing production quality assessment index is greater than 0.6 and less than 0.4, it means that the production status belongs to the third level; It means that the production status is at an average level, indicating that there are obvious shortcomings. It is necessary to specifically analyze whether there are problems with printing quality, printing efficiency and equipment status.

[0036] A4: When the printing production quality assessment index is less than 0.4, it means that the production status belongs to the fourth level.

[0037] It indicates that the production status is at a poor level and immediate intervention is required to avoid systemic risks.

[0038] In this embodiment, it is necessary to specifically explain the formulation of a production optimization strategy. If quality defects occur frequently, for example, the plate sticking defect rate is greater than 1% and the color qualification rate is less than 80%, it is necessary to automatically adjust the ink viscosity and printing pressure, increase the scanning frequency, mark the current batch of products, and start the batch traceability process; if an efficiency bottleneck occurs, for example, the time utilization rate is less than 60% and the plate changing efficiency is less than 0.5, it is necessary to push a standardized video of the plate changing operation to the human-computer interaction terminal to guide the production site staff, analyze similar production historical data, and recommend the optimal process parameter combination; when equipment failure warning and a sudden drop in quality index occur at the same time, equipment shutdown protection is executed first, production is suspended, and the emergency maintenance process is started to avoid further losses.

[0039] If the predicted probability of short-term printing equipment failure is greater than 80%, it is necessary to reduce the equipment operating speed, reduce the mechanical load, notify maintenance personnel to prepare spare parts, adjust the production schedule, and migrate subsequent orders to spare equipment.

[0040] S5: Automatic control execution: The edge computing node receives the printing production optimization strategy, generates equipment adjustment instructions, and sends them to the automatic control terminal, which automatically adjusts the parameters of the printing production equipment according to the equipment adjustment instructions; In this embodiment, it should be specifically explained that the instructions generated by the edge computing node include parameter adjustment class and device action class, and the standardized instructions are converted into the device native protocol, and the instructions are subjected to parameter boundary check, device status check and security protection mechanism activation, and the automatic control terminal performs analog control, digital control and multi-device collaborative control to achieve closed-loop control.

[0041] S6: Feedback on execution results: Use the data acquisition terminal to collect the adjusted multi-dimensional printing production data in real time, feed it back to the cloud server, and display the data of the printing production process in real time through the human-computer interaction terminal.

[0042] In this embodiment, it is necessary to specifically explain that the adjusted multi-dimensional printing production data is monitored in real time, and data synchronization is performed, and the adjusted printing production quality evaluation index is calculated using a cloud server to determine whether the adjustment effect is effective.

[0043] The visual interface of the human-computer interaction terminal includes a main monitoring screen and a mobile APP. The main monitoring screen includes an equipment status dashboard, a quality trend chart, and an efficiency data wall. The equipment status dashboard is used to display the vibration speed, temperature, and operating status of each printing press in real time. The quality trend chart is used to dynamically draw the overprint error and warning threshold line in real time. The efficiency data wall scrolls to display the calculated data; the mobile APP includes a personalized dashboard, exception push, and historical data query. The personalized dashboard displays the data of interest by role. For example, quality engineers focus on defect rate, equipment managers focus on vibration data, exception push pushes warning information in the form of pictures, text, and voice. The historical data query supports the retrieval of historical production data by time, equipment, and order number, and generates PDF format reports.

[0044] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for real-time collection and analysis of printing production data, characterized in that: Including data acquisition terminal, edge computing node, cloud server, automatic control terminal and mobile interactive terminal, the steps are as follows: S1: Multi-source data acquisition: The data acquisition terminal collects multi-dimensional printing production data in real time, including printing quality data, printing efficiency data, and equipment status data, and transmits the real-time collected data to the edge computing node; S2: Real-time edge processing: The edge computing node performs data cleaning and feature extraction on the multi-dimensional printing production data to obtain a multi-dimensional printing production data set, performs real-time analysis using preset thresholds, and transmits the multi-dimensional printing production data set to the cloud server; S3: Data in-depth analysis: The cloud server receives and stores the printing production data set, builds a printing production data analysis model, calculates the printing production quality evaluation index, and builds a prediction model to predict the probability of printing equipment failure; S4: Optimization strategy generation: The printing production quality is judged based on the printing production quality evaluation index. The printing production optimization strategy is formulated based on the failure probability of printing equipment and the printing production quality, and is sent to the edge computing node. S5: Automatic control execution: The edge computing node receives the printing production optimization strategy, generates equipment adjustment instructions, and sends them to the automatic control terminal, which automatically adjusts the parameters of the printing production equipment according to the equipment adjustment instructions; S6: Feedback on execution results: Use the data acquisition terminal to collect the adjusted multi-dimensional printing production data in real time, feed it back to the cloud server, and display the data of the printing production process in real time through the human-computer interaction terminal.

2. The method for real-time collection and analysis of printing production data according to claim 1, characterized in that: The printing quality data includes color qualification rate, overprint error, number of paste-plate prints, total print quantity, dot gain rate and number of defective prints; the printing efficiency data includes effective production time, number of prints produced and plate change efficiency; the equipment status data includes vibration speed, maximum roller temperature, ambient temperature, roller length, operating time and number of failures.

3. The method for real-time collection and analysis of printing production data according to claim 1, characterized in that: The data cleaning specifically includes outlier removal, missing value processing and data standardization. Feature extraction specifically includes time domain feature extraction, frequency domain feature extraction and image feature extraction. The thresholds specifically include color deviation threshold, overprint error threshold, vibration speed threshold and roller temperature threshold. The edge computing node generates equipment adjustment instructions by comparing the real-time received color difference qualified rate, overprint error, vibration speed and roller temperature with the preset thresholds, and transmits them to the automatic control terminal to adjust the production parameters of the printing equipment in real time.

4. The method for real-time collection and analysis of printing production data according to claim 1, characterized in that: The calculation of the printing production quality evaluation index requires the printing quality evaluation index y1, the printing efficiency evaluation index y2 and the equipment status evaluation index y3, and the printing quality evaluation index, the printing efficiency evaluation index and the equipment status evaluation index are calculated using the formula The printing production quality evaluation index y is calculated, where d1, d2, and d3 represent the weight coefficients of the printing quality evaluation index, the printing efficiency evaluation index, and the equipment status evaluation index, respectively, and the total is 1.

5. The method for real-time collection and analysis of printing production data according to claim 4, characterized in that: The acquisition of the printing quality evaluation index requires setting a time window, and obtaining the color qualification rate η of a single printed product in the printing production data set within the time window. 1i , overprint error d i and dot gain ratio r i , get the color pass rate mean η1 and the overprint error mean d, calculate the ratio of the difference between the single dot enlargement rate and the planned dot enlargement rate r to the planned dot enlargement rate using the formula Get the dot gain deviation r ai , and get the dot gain rate deviation mean r a , obtain the number of smeared prints n1, the total number of prints n, and the number of defective prints n2 in the printing production data set within the time window, and calculate the ratio of the number of smeared prints and the number of defective prints to the total number of prints to obtain the smeared print defect rate r h and the total defect rate r b , the color qualification rate, overprint error, dot gain deviation, smear printing defect rate and total defect rate are calculated through the formula: ， Get the printing quality evaluation index y1, r 标 Indicates the standard dot gain deviation, Δr max Indicates the maximum allowable dot gain deviation, d max Indicates the maximum permissible overprint error, r hmax Indicates the maximum allowable smear printing defect rate, r bmax represents the maximum allowable total defect rate, a1, a2, a3, a4, and a5 represent the corresponding weight coefficients, and the total is 1.

6. The method for real-time collection and analysis of printing production data according to claim 4, characterized in that: The acquisition of the printing efficiency evaluation index requires obtaining the effective production time t, the number of printed products N, and the plate change efficiency η2 in the printing production data set within the time window, and calculating the ratio of the effective production time to the maximum theoretical production time to obtain the time utilization rate η t , calculate the ratio of the number of printed products produced to the theoretical maximum number of printed products produced to obtain the performance utilization rate η N , using the time utilization, performance utilization and plate change efficiency formula: ， Get the printing efficiency evaluation index y2, where η 2min and η 2max They represent the minimum and maximum values ​​of the plate changing efficiency respectively. b1, b2 and b3 represent the weight coefficients of time utilization, performance utilization and plate changing efficiency respectively, and their total is 1.

7. The method for real-time collection and analysis of printing production data according to claim 4, characterized in that: The acquisition of the equipment status evaluation index requires the vibration speed V, the maximum roller temperature T in the printing production data set within the time window. 1m , ambient temperature T2, drum length l, operating time t1 and number of failures n3, using the maximum drum temperature, ambient temperature and drum length using the formula The temperature gradient D is calculated, and the ratio of the operating time to the number of failures is calculated to obtain the mean time between failures t w , using vibration velocity, temperature gradient and mean time between failures through the formula: ， Get the equipment status evaluation index y3, where exp represents the exponential decay model, V 临界 represents the critical value of the vibration frequency, G1 is a piecewise linear model, t wmin and t wmax They represent the minimum and maximum values ​​of the mean time between failures, respectively. c1, c2 and c3 represent the weight coefficients of vibration velocity, temperature gradient and mean time between failures, respectively, and their total is 1.

8. The method for real-time collection and analysis of printing production data according to claim 1, characterized in that: The judgment of the printing production quality requires the construction of a production quality assessment threshold. The printing production quality assessment index calculated in real time is compared with the production quality assessment threshold, and the production status levels are divided into the first level, the second level, the third level and the fourth level from high to low. The production optimization strategy is formulated in combination with the production status level and the probability of printing equipment failure.

9. The method for real-time collection and analysis of printing production data according to claim 8, characterized in that: The steps of the production status level determination process are as follows: A1: When the printing production quality assessment index is greater than 0.8 and less than 1, it indicates that the production status belongs to the first level; A2: When the printing production quality assessment index is greater than 0.6 and less than 0.8, it indicates that the production status belongs to the second level; A3: When the printing production quality assessment index is greater than 0.6 and less than 0.4, it means that the production status belongs to the third level; A4: When the printing production quality assessment index is less than 0.4, it means that the production status belongs to the fourth level.

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