Printing fault data intelligent analysis method and system

Abstract

The invention discloses a printing fault data intelligent analysis method and system, and belongs to the technical field of fault monitoring, and the method comprises the steps: firstly collecting raw material, environment and image data in printing production to construct a fault parameter library, combing faults to construct a risk fault library, and analyzing the correlation of the two to determine pre-printing wind evaluation parameters; calculating a wind evaluation value, and matching the collection frequency of a wind evaluation parameter set; calculating an in-print evaluation value through a sliding time window, updating the collection frequency, and carrying out risk fault early warning according to a fault evaluation value; and finally, constructing a maintenance personnel database, and screening out the most suitable maintenance personnel to process the fault according to the maintenance case and the early warning fault analysis evaluation value. According to the invention, fault factor positioning and risk assessment quantification are facilitated, data acquisition dynamic adjustment and accurate early warning are realized, and maintenance resources are efficiently allocated, so that the quality and efficiency of printing production are improved, and the comprehensive competitiveness of enterprises in the market is enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of fault monitoring, and particularly relates to a printing fault data intelligent analysis method and system. BACKGROUND

[0002] In the printing industry, with the progress of technology and the growth of market demand, the scale of printing production is continuously expanding, the product types are increasingly diversified, and the requirements for printing quality and production efficiency are also increasingly high. However, the existing printing fault processing technology has obvious deficiencies. There are deficiencies in collating the correlations between various faults in printing production and raw materials, environment, and image data, which makes it difficult to accurately identify key factors that may cause faults in the pre-press stage, thereby affecting the ability to take targeted preventive measures in advance. There are limitations in dynamically adjusting the data collection frequency according to fault risks, which makes it difficult to monitor the risk changes in the printing process in real time, thereby affecting the ability to capture early signs of faults in a timely manner, and further affecting the timeliness and accuracy of fault warning. There are deficiencies in quantitatively evaluating fault risks, which makes it difficult to intuitively reflect the possibility of fault occurrence, thereby affecting the ability of enterprises to reasonably arrange production resources and preventive measures according to risk priorities, and further affecting the overall efficiency and quality of printing production. Therefore, we propose a printing fault data intelligent analysis method and system. SUMMARY

[0003] The purpose of the present application is to provide a printing fault data intelligent analysis method and system to solve the problems raised in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a printing fault data intelligent analysis method, comprising the following steps Step one: build a fault parameter library and a fault parameter library, analyze the correlation between each risk fault in the fault parameter library and each fault parameter in the fault parameter library, and analyze the pre-press evaluation parameters corresponding to each risk fault based on this analysis; Step two: analyze the pre-press evaluation value of the risk fault according to the pre-press evaluation parameters corresponding to the risk fault; build a set of in-process evaluation parameters corresponding to the risk fault, and match the collection frequency of the corresponding in-process evaluation parameter set according to the pre-press evaluation value of the risk fault; Step three: set a sliding time window, calculate the in-process evaluation value of the in-process evaluation parameter in the sliding time window, update the collection frequency of the in-process evaluation parameter according to the in-process evaluation value, analyze the in-process evaluation value of each in-process evaluation parameter of the risk fault, obtain the fault evaluation value, and determine whether to perform risk fault warning according to the fault evaluation value; Step four, constructing a maintenance personnel database, according to the maintenance cases of the maintenance personnel and the risk failure analysis of the maintenance personnel corresponding to the current risk failure early warning, the maintenance personnel corresponding to the maximum selection evaluation value are selected as the task dispatch personnel.

[0005] Preferably, in step one, the specific process of constructing the fault parameter library is as follows: Collecting the raw material data, printing environment data and printing image data in the current printing production, and organizing and constructing the pre-press fault parameter library; the raw material data includes: paper thickness, ink fluidity, plate wear resistance; the printing environment data includes: workshop temperature and humidity, cleanliness, air pressure; the printing image data includes: color number, dot distribution density, image fineness; Combining all printing faults caused by pre-press raw material data, printing environment data and printing image data, and organizing and constructing the risk failure library.

[0006] Preferably, in step one, the specific process of analyzing the correlation between each risk failure in the fault parameter library and each fault parameter in the fault parameter library is as follows: For each risk failure in the risk failure library, a rectangular coordinate system of risk failure and fault parameter is established, wherein the risk failure degree is the vertical coordinate and the fault parameter is the horizontal coordinate; then, from the large amount of data recorded in the process log of the printing production, the one-to-one corresponding data points of the fault parameter and the risk failure are extracted; these data points are labeled one by one in the established rectangular coordinate system to obtain the analysis scatter plot; The risk failure degree is divided into m intervals and represented as C1, C2, …, Cm; at the same time, the fault parameter is divided into n intervals and represented as A1, A2, …, An; the number of data points falling into each intersection area of the risk failure degree interval and the fault parameter interval is counted and marked as , wherein i represents the label of the risk failure degree interval and j represents the label of the fault parameter interval; the formula is used to obtain the total number of data points in all intersection areas ; the joint probability of being in the risk failure degree interval Ci and being in the fault parameter interval Aj ; the marginal probability of being in the risk failure degree interval Ci ; and the marginal probability of being in the fault parameter interval Aj ; The above , , and are used to obtain the mutual information value MI by using the mutual information formula: ​Each fault parameter and the fault parameter information value corresponding to the risk fault are compared with the corresponding threshold. If the fault parameter information value is greater than the corresponding threshold, the fault parameter corresponding to the fault parameter information value is marked as the prepress risk assessment parameter of this risk fault. At the same time, based on the scatter plot of each fault parameter and the corresponding risk fault, the positive and negative correlations between the fault parameters and the corresponding risk faults are marked.

[0007] Preferably, in step two, the specific process of analyzing the risk assessment value of a risk fault based on the prepress risk assessment parameters corresponding to the risk fault is as follows: For each risk failure, obtain the values ​​of various prepress risk assessment parameters corresponding to the risk failure, and mark the prepress risk assessment parameters that are positively correlated with the risk failure as follows: r = 1, 2, ..., P; where P is the total number of prepress risk assessment parameters positively correlated with risk failures; prepress risk assessment parameters negatively correlated with risk failures are labeled as... a = 1, 2, ..., K; where K is the total number of prepress risk assessment parameters that are negatively correlated with risk failures; after normalizing all prepress risk assessment parameter values, the formula is used: The reputation score FP is obtained, among which Preset weighting coefficients for prepress risk assessment parameters that are positively correlated with risk failures; Preset weighting coefficients for prepress risk assessment parameters that are negatively correlated with risk failures.

[0008] Preferably, in step two, a set of India-China risk assessment parameters corresponding to risk faults is constructed, and the collection frequency of the corresponding India-China risk assessment parameter set is matched according to the risk assessment value of the risk fault. The specific process is as follows: All risk faults in the risk fault database are sorted in descending order according to their corresponding risk assessment values ​​to obtain a risk fault sorting sequence, and the risk fault sorting sequence is sent to the monitoring personnel's terminal. Each type of risk fault has a corresponding set of print quality assessment parameters. Based on the risk fault sorting sequence of the current printing material, each risk fault is matched with the print quality assessment parameter set corresponding to all risk faults in turn, and the print quality assessment parameter set corresponding to each risk fault is output. For each risk fault, several risk assessment value ranges are preset, and each risk assessment value range corresponds to a printing risk assessment parameter collection frequency. The risk assessment value of the risk fault is matched with all preset risk assessment value ranges, and its corresponding printing risk assessment parameter collection frequency is output. This printing risk assessment parameter collection frequency is used as the collection frequency of each risk assessment parameter in the printing risk assessment parameter set for that risk fault during the printing process.

[0009] Preferably, in step three, the specific process of setting a sliding time window, calculating the China-India rating value of the China-India rating parameter within the sliding time window, and updating the collection frequency of the China-India rating parameter based on the China-India rating value is as follows: In the printing process, for each risk fault, the values ​​of each print risk assessment parameter in the print risk assessment parameter set are collected according to the collection frequency corresponding to the print risk assessment parameter set of the risk fault; thus obtaining the value of each print risk assessment parameter at each collection time. For each India-China reputation parameter, a sliding time window is set, and the mean, standard deviation, and range of the India-China reputation parameter within the sliding window are calculated. Then, different weight coefficients are assigned to the three, and the mean, standard deviation, and range of the India-China reputation parameter are multiplied by the corresponding weight coefficients and then summed to obtain the India-China evaluation value. Multiple preset frequency update intervals for the India-China evaluation values ​​are used to continuously compare the frequency update intervals of the India-China evaluation values ​​in the current sliding window with those in the previous sliding window. Once a jump in the frequency update interval of the India-China evaluation values ​​in the current sliding window is detected, a frequency update operation is triggered. Each India-China assessment value sampling frequency update interval corresponds to an India-China reputation parameter sampling update frequency. When the sampling update operation is triggered, the sampling update frequency corresponding to the sampling frequency update interval of the India-China assessment value in the current sliding window is used as the sampling frequency of the India-China reputation parameter for the next period of time. The sampling frequency is then continuously updated according to the above-mentioned sampling frequency update process. The print evaluation value within each sliding time window is continuously calculated and the interval jump situation is judged. If the interval jump occurs again, the sampling frequency is updated again.

[0010] Preferably, in step three, the print assessment values ​​of each print risk parameter are analyzed to obtain the fault assessment value. The specific process for determining whether to issue a risk fault warning based on the fault assessment value is as follows: For each India-China risk assessment parameter, multiple risk ranges for India-China assessment values ​​are set. Each risk range corresponds to a pre-defined risk assignment. The India-China assessment value corresponding to the current India-China risk assessment parameter is matched with all risk ranges for India-China assessment values, and the corresponding risk assignment is output. ; g = 1, 2, ..., H; g is the label of the India-China wind rating parameter, and H is the total number of India-China wind rating parameters; Substitute the risk values ​​corresponding to each India-China risk assessment parameter for the risk fault into the preset formula model: Therefore, the evaluation value GP is obtained, where Assign preset weight coefficients to the risk assessment parameters for each India-China risk assessment parameter; A two-dimensional rectangular coordinate system is established with the fault evaluation value as the vertical coordinate and time as the horizontal coordinate, the numerical value of the risk fault fault evaluation value is calculated in real time, and is substituted into the two-dimensional rectangular coordinate system to obtain a series of fault evaluation data points, the adjacent fault evaluation data points are sequentially connected by a curve to obtain a fault evaluation change curve, a preset fault evaluation threshold line is provided, and an area surrounded by the part of the fault evaluation change curve exceeding the fault evaluation threshold line and the fault evaluation threshold line is marked as a wind evaluation area; All wind evaluation areas from the start time to the current time are accumulated to obtain an instant risk accumulation, a preset instant risk accumulation threshold is provided, and if the instant risk accumulation is greater than or equal to the corresponding threshold, the risk fault early warning is triggered.

[0011] Preferably, in step four, the specific process of constructing the maintenance personnel database is: A maintenance personnel database is constructed to record personal information, maintenance cases and maintenance scheduling of the maintenance personnel; the personal information includes name, ID card, personnel image and contact information; The maintenance cases store the names of the risk faults that can be maintained by the maintenance personnel, and the number of processing, average maintenance time and maintenance performance score of each risk fault; When the risk fault early warning occurs, all risk faults corresponding to the early warning are obtained, personnel in the current maintenance scheduling are screened from the maintenance personnel database, the maintenance cases thereof are extracted, and the maintenance personnel who have processed all the early warning risk faults are selected as the preliminary selected personnel.

[0012] Preferably, in step four, the specific process of analyzing the selection evaluation value of the maintenance personnel and screening the maintenance personnel corresponding to the maximum selection evaluation value as the task dispatch personnel is: For the preliminary selected personnel, the processing number SLz, the average maintenance time WTz and the maintenance performance score JXz of each early warning risk fault in the maintenance cases thereof are comprehensively considered, wherein z is the mark of the risk fault, z=1, 2, …, D; D is the total number of risk faults; after the processing number SLz, the average maintenance time WTz and the maintenance performance score JXz of the risk fault are normalized, the formula: is used to obtain the evaluation value WP, wherein is the minimum value in the average maintenance time of all risk faults b is a loop variable used for summing the processing number of the risk fault, b=1, 2, …, D; , , are the weight coefficients of the processing number, the average maintenance time and the maintenance performance score of the risk fault, respectively; The number of risk faults that have not been processed and completed by the preliminary selected personnel is obtained, the task load RF is obtained by multiplying each risk fault that has not been processed and completed by the average maintenance time of the corresponding risk fault and then adding them together; For each primary candidate, the corresponding evaluation value WP of the primary candidate is normalized with the task load RF, and the selected evaluation value XPZ is obtained by using the formula: , wherein d1 and d2 are preset weight coefficients. The primary candidate with the maximum selected evaluation value is selected from all the primary candidates as a task dispatching person to handle the risk failure generated by the current early warning; after the task dispatching person successfully solves the risk failure, maintenance performance scoring work is carried out for each risk failure handled by the task dispatching person, and the obtained score is recorded in the maintenance performance score record of the risk failure in the maintenance case of the person.

[0013] Compared with the prior art, the beneficial effects of the present application are: (1) The printing fault data intelligent analysis method and system constructs a fault parameter library by collecting data of raw materials, environment and images in printing production, sorts out various faults to construct a risk failure library, and deeply analyzes the correlation between the two; this process facilitates finding the prepress wind evaluation parameters closely related to each risk failure, so that the printing enterprise can clearly recognize the key factors that may cause faults in the early production stage, and provides a strong basis for targeted prevention of faults.

[0014] (2) The printing fault data intelligent analysis method and system quantifies the risk by calculating the wind evaluation value of the risk failure according to the prepress wind evaluation parameter, and directly reflects the probability of fault occurrence; the wind evaluation value is used to construct a set of in-print wind evaluation parameters and match the collection frequency, so as to avoid blind data collection and realize reasonable allocation of resources; at the same time, the risk failure is sent to the monitoring personnel in order of wind evaluation value, which facilitates the monitoring personnel to quickly understand the fault priority and pay special attention to high-risk faults, provides clear risk reference for printing production, helps the enterprise to make scientific decisions, and reasonably arranges production resources and preventive measures.

[0015] (3) The printing fault data intelligent analysis method and system calculates the in-print evaluation value by setting a sliding time window, and updates the in-print wind evaluation parameter collection frequency according to the interval where the evaluation value is located, realizes dynamic adjustment of data collection frequency, and captures small fluctuations of faults in time; the evaluation value is calculated by comprehensively considering the mean, standard deviation and range value of the in-print wind evaluation parameter, which is combined with risk assignment analysis to facilitate accurate reflection of the probability of risk failure occurrence; the real-time risk product is calculated by establishing a two-dimensional rectangular coordinate system and compared with a preset threshold, which realizes accurate early warning, enables the enterprise to take measures in advance, reduces production interruption and quality problems, and guarantees production continuity and product quality.

[0016] (4), the printing fault data intelligent analysis method and system, the maintenance personnel database is constructed, the risk fault early warning is combined with screening task dispatch personnel;Comprehensively consider the processing quantity, average maintenance time and performance score and other multi-dimensional data in the maintenance case of maintenance personnel, and the current task load, calculate the selected evaluation value to determine the most suitable maintenance personnel, improve the utilization efficiency of maintenance resources;Maintenance performance scoring and record updating after task completion provide continuous data support for maintenance personnel capability evaluation, which helps to optimize maintenance team management and ensure that the fault is handled in time and effectively. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. EMBODIMENT

[0019] Please refer to Figure 1 The present application provides a printing fault data intelligent analysis method, comprising; Step 1: Constructing fault parameter library and fault parameter library, analyzing the correlation between each risk fault in the fault parameter library and each fault parameter in the fault parameter library, and based on this analysis, corresponding prepress wind evaluation parameters of each risk fault, the specific process is as follows: Collect raw material data, printing environment data and printing image data in current printing production, and after preprocessing the above collected data, organize and construct prepress fault parameter library;Raw material data includes: paper thickness, ink fluidity, wear resistance of plate material, etc;Printing environment data includes: workshop temperature and humidity, cleanliness, air pressure, etc;Printing image data includes: color number, dot distribution density, image fineness, etc; Comb all printing faults caused by prepress raw material data, printing environment data and printing image data, and organize and construct risk fault library;The risk fault library contains: misregistration, uneven ink color, paper wrinkling, dot blurring, etc; For each risk fault in the risk fault library, a rectangular coordinate system of risk fault and fault parameter is established, wherein the risk fault degree is the ordinate and the fault parameter is the abscissa;Then, from the large amount of data recorded in the process log of printing production, the one-to-one corresponding data points of the fault parameter and the risk fault are extracted;These data points are labeled one by one in the constructed rectangular coordinate system, and an analysis scatter plot is obtained; Divide the risk of failure into m intervals, denoted as C1, C2, ..., Cn. m Simultaneously, the fault parameters are divided into n intervals, denoted as A1, A2, ..., A n Count the number of data points that fall within the intersection area of ​​each risk failure severity interval and failure parameter interval, and mark them as follows: Where i represents the label of the risk level interval and j represents the label of the fault parameter interval; using the formula: To obtain the total number of data points in all intersecting regions. The joint probability of being in the risk fault level interval Ci and the fault parameter interval Aj Marginal probability of the risk failure level range Ci ; and the marginal probability of being in the fault parameter range Aj ; The above , , as well as Using the mutual information formula: The fault parameter information value MI is obtained, where the fault parameter information value represents the mutual information value between the fault parameter and the risk fault. The larger the fault parameter information value, the stronger the dependence between the fault parameter and the risk fault. A preset fault parameter information threshold is set. The fault parameter information value corresponding to each fault parameter and risk fault is compared with the corresponding threshold. If the fault parameter information value is greater than the corresponding threshold, the fault parameter corresponding to the fault parameter information value is marked as the prepress risk assessment parameter of this risk fault. At the same time, based on the scatter plot of each fault parameter and the corresponding risk fault, the positive and negative correlations between the fault parameters and the corresponding risk faults are marked.

[0020] It should be noted that by collecting data from various aspects of printing production to build a fault parameter database, sorting out various faults to build a risk fault database, and deeply analyzing the correlation between the two, it is possible to accurately identify the pre-press risk assessment parameters closely related to each risk fault. This allows for a clear understanding of the key factors that may cause faults in the early stages of printing production. At the same time, the determination of pre-press risk assessment parameters lays an important foundation for fault prediction and intelligent analysis in the subsequent printing process.

[0021] Step Two: Analyze the risk assessment value of the risk fault based on the pre-press risk assessment parameters corresponding to the risk fault; construct the in-press risk assessment parameter set corresponding to the risk fault, and match the collection frequency of the corresponding in-press risk assessment parameter set according to the risk fault's risk assessment value. The specific process is as follows: For each risk failure, obtain the values ​​of various prepress risk assessment parameters corresponding to the risk failure, and mark the prepress risk assessment parameters that are positively correlated with the risk failure as follows: , r = 1, 2, …, P; wherein P is the total number of pre-printing wind evaluation parameters positively correlated with the risk failure; pre-printing wind evaluation parameters negatively correlated with the risk failure are marked as , a = 1, 2, …, K; wherein K is the total number of pre-printing wind evaluation parameters negatively correlated with the risk failure; after normalizing all pre-printing wind evaluation parameter values, the wind evaluation value FP is obtained by using the formula: , wherein is the preset weight coefficient of the pre-printing wind evaluation parameter positively correlated with the risk failure; is the preset weight coefficient of the pre-printing wind evaluation parameter negatively correlated with the risk failure; the larger the wind evaluation value, the greater the possibility of risk failure; all risk failures in the risk failure library are sorted according to the corresponding wind evaluation value from large to small to obtain a risk failure sorting sequence, and the risk failure sorting sequence is sent to the terminal of the monitoring personnel; a corresponding pre-printing wind evaluation parameter set is preset for each risk failure; according to the risk failure sorting sequence of the current printing material, each risk failure is matched with the pre-printing wind evaluation parameter set corresponding to all risk failures in sequence, and the pre-printing wind evaluation parameter set corresponding to each risk failure is output; wherein the method of analyzing the pre-printing wind evaluation parameter set corresponding to the risk failure analysis can be analyzed by the correlation method of analyzing the pre-printing wind evaluation parameter of each risk failure in step one; for each risk failure, a plurality of wind evaluation value intervals are preset, and each wind evaluation value interval corresponds to a pre-printing wind evaluation parameter collection frequency; the wind evaluation value of the risk failure is matched with all preset wind evaluation value intervals, and the corresponding pre-printing wind evaluation parameter collection frequency is output, and the pre-printing wind evaluation parameter collection frequency is used as the collection frequency of each wind evaluation parameter in the pre-printing wind evaluation parameter set of the risk failure.

[0022] It should be noted that by calculating the wind evaluation value, the possibility of risk failure is quantified, which can intuitively reflect the probability of the failure and provide a clear risk reference for printing production; according to the wind evaluation value, the collection frequency of the pre-printing wind evaluation parameter set is matched, which avoids blind data collection, saves system resources, and realizes reasonable allocation of resources; risk failures are sorted according to wind evaluation values and sent to monitoring personnel, so that monitoring personnel can quickly understand the priority of various risk failures in printing production and focus on high-risk failures; at the same time, the pre-printing wind evaluation parameter set and the collection frequency are determined, which provides a key data basis for subsequent real-time monitoring and failure warning, and helps to discover hidden troubles in advance and take measures.

[0023] Step three: set a sliding time window, calculate the printing evaluation value of the printing wind evaluation parameter in the sliding time window, update the collection frequency of the printing wind evaluation parameter according to the printing evaluation value; analyze the printing evaluation value of each printing wind evaluation parameter of the risk failure, obtain the fault evaluation value, and analyze the fault evaluation value to determine whether to perform risk failure early warning, and the specific process is: In the printing work, for each risk failure, the values of each printing wind evaluation parameter in the printing wind evaluation parameter set are collected according to the collection frequency corresponding to the printing wind evaluation parameter set of the risk failure; the values of each printing wind evaluation parameter corresponding to each collection time are obtained; For each printing wind evaluation parameter, a sliding time window is set, and the size and sliding step of the sliding time window are preset, wherein the size and sliding step of the sliding time window can be set according to the collection frequency of the wind evaluation parameter. The faster the collection frequency, the smaller the window size and step, that is, enough data can be collected. The mean, standard deviation and range of the printing wind evaluation parameter in the sliding window are calculated, and then the three are assigned different weight coefficients. The mean, standard deviation and range of the printing wind evaluation parameter are multiplied by the corresponding weight coefficients and then accumulated to obtain the printing evaluation value. A plurality of printing evaluation value sampling frequency update intervals are preset, and the printing evaluation value sampling frequency update intervals in the current sliding window and the previous sliding window are continuously compared; once it is found that the printing evaluation value in the current sliding window jumps from one interval to another, the sampling frequency update operation is triggered; Each printing evaluation value sampling frequency update interval corresponds to a printing wind evaluation parameter collection update frequency, and when the sampling frequency update operation is triggered, the collection update frequency corresponding to the sampling frequency update interval in which the printing evaluation value in the current sliding window is located is used as the collection frequency of the printing wind evaluation parameter in the next period of time; the above judgment and collection frequency update process is continuously performed to continuously calculate the printing evaluation value in each sliding time window and judge the interval jump situation. If the interval jump occurs again, the collection frequency is updated again. For each printing wind evaluation parameter, a plurality of printing evaluation value risk intervals are set, each risk interval corresponds to a risk value, and the printing evaluation value corresponding to the current time of the printing wind evaluation parameter is matched with all printing evaluation value risk intervals to output the corresponding risk value ; g = 1, 2, …, H; g is the label of the printing wind evaluation parameter, and H is the total number of the printing wind evaluation parameters; The risk value corresponding to each printing wind evaluation parameter of the risk failure is substituted into the preset formula model: , to obtain the fault evaluation value GP, wherein is the preset weight coefficient corresponding to the risk value of each printing wind evaluation parameter, and the larger the fault evaluation value, the greater the probability of occurrence of the risk failure in the printing work. A two-dimensional rectangular coordinate system is established with the fault evaluation value as the ordinate and time as the abscissa, the numerical value of the risk fault fault evaluation value is calculated in real time, and is substituted into the two-dimensional rectangular coordinate system to obtain a series of fault evaluation data points. The adjacent fault evaluation data points are sequentially connected by a curve to obtain a fault evaluation change curve. A fault evaluation threshold line is preset, and an area surrounded by the part of the fault evaluation change curve that exceeds the fault evaluation threshold line and the fault evaluation threshold line is marked as a wind evaluation area. All wind evaluation areas from the start time to the current time are accumulated to obtain an instant risk accumulation. An instant risk accumulation threshold is preset. If the instant risk accumulation is greater than or equal to the corresponding threshold, the risk fault early warning is triggered.

[0024] It should be noted that the in-process evaluation value is calculated by the sliding time window, and the collection frequency of the in-process wind evaluation parameter is updated according to the jump of the interval in which the evaluation value is located, so that the data collection frequency is dynamically adjusted. It is convenient to more flexibly cope with the change of the risk fault state in the printing process and timely capture the slight fluctuation that may indicate the occurrence of a fault. The in-process evaluation value is calculated by comprehensively considering the mean, standard deviation and range of the in-process wind evaluation parameter, which comprehensively considers the concentration tendency, dispersion degree and change range of the data, and then the risk evaluation value is obtained by combining the risk evaluation analysis. The risk fault evaluation value reflects the probability of the occurrence of the risk fault in the printing work. It is helpful for the printing enterprise to take measures in advance to reduce the possibility of the occurrence of the fault and reduce the production interruption and quality problems caused by the fault. The two-dimensional rectangular coordinate system is established, the instant risk accumulation is calculated, and the risk fault early warning is triggered by comparing the instant risk accumulation with the preset threshold, which can intuitively show the change trend of the risk fault with time. Once the instant risk accumulation exceeds the threshold, the system immediately issues an early warning, giving the printing enterprise sufficient time to take countermeasures.

[0025] Step four, constructing a maintenance personnel database, selecting the maintenance personnel corresponding to the maximum selection evaluation value as the task dispatch personnel according to the maintenance cases of the maintenance personnel and the selection evaluation value of the maintenance personnel corresponding to the risk fault analysis of the current risk fault early warning, the specific process is: A maintenance personnel database is constructed to record the personal information, maintenance cases and maintenance scheduling of the maintenance personnel. The personal information includes name, ID card, personnel image, contact information, etc. The maintenance cases store the names of the risk faults that the maintenance personnel can maintain, the number of each risk fault processing, the average maintenance time, and the maintenance performance score, etc. When the risk fault early warning occurs, all risk faults corresponding to the early warning are obtained, the personnel in the current maintenance scheduling are selected from the maintenance personnel database, the maintenance cases are extracted, and the maintenance personnel who have processed all the early warning risk faults are selected as the preliminary selected personnel. For the preliminary selected personnel, the processing quantity SLz, the average maintenance time WTz and the maintenance performance score JXz of each early warning risk failure in the maintenance case of the preliminary selected personnel are integrated, wherein z is the label of the risk failure, z=1, 2, …, D; D is the total number of risk failures; after the processing quantity SLz, the average maintenance time WTz and the maintenance performance score JXz of the risk failure are normalized, the maintenance evaluation value WP is obtained by using the formula: , wherein is the minimum value of the average maintenance time of all risk failures , b is a loop variable for summing the processing quantity of the risk failure, b=1, 2, …, D; , , , respectively, are the weight coefficients of the processing quantity, the average maintenance time and the maintenance performance score of the risk failure; The number of risk failures that have not been processed and completed by the preliminary selected personnel is obtained, and the task load RF is obtained by adding the average maintenance time of each risk failure that has not been processed and completed; For each preliminary selected personnel, the maintenance evaluation value WP corresponding to the preliminary selected personnel and the task load RF are normalized, and the selection evaluation value XPZ is obtained by using the formula: , wherein d1 and d2 are preset weight coefficients, and the larger the selection evaluation value of the preliminary selected personnel is, the more suitable the preliminary selected personnel is for being arranged to process the risk failure corresponding to the early warning of the fault; The preliminary selected personnel with the largest selection evaluation value is selected from all preliminary selected personnel as the task dispatch personnel to operate and maintain the risk failure generated by the early warning; after the task dispatch personnel successfully solves the risk failure, maintenance performance scoring work is carried out for each risk failure processed by the task dispatch personnel, and the obtained score is recorded in the maintenance performance score record of the corresponding risk failure in the maintenance case of the personnel.

[0026] It should be noted that constructing the maintenance personnel database and selecting the task dispatch personnel in combination with the risk failure early warning can accurately find the most suitable person to process the current fault from a large number of maintenance personnel; by comprehensively considering the multi-dimensional data such as the processing quantity, the average maintenance time and the performance score in the maintenance case, and the current task load, the selection evaluation value is calculated to determine the dispatched personnel, which improves the utilization efficiency of maintenance resources and ensures that the fault can be processed in time and effectively; the maintenance performance scoring and record updating after the completion of the task provide continuous data support for the ability evaluation of the maintenance personnel.

[0027] A printed fault data intelligent analysis system, comprising: a fault parameter correlation analysis module, a wind evaluation assessment and data collection module, an evaluation early warning module and a maintenance personnel scheduling module, specifically: The fault parameter correlation analysis module: constructs a fault parameter library and a fault parameter library, analyzes the correlation between each risk fault in the fault parameter library and each fault parameter in the fault parameter library, and analyzes the corresponding pre-printing wind evaluation parameters of each risk fault based on the analysis; The wind evaluation assessment and data collection module: analyzes the wind evaluation value of the risk fault according to the pre-printing wind evaluation parameters corresponding to the risk fault; constructs a set of in-print wind evaluation parameters corresponding to the risk fault, and matches the collection frequency of the corresponding in-print wind evaluation parameter set according to the wind evaluation value of the risk fault; The evaluation and early warning module: sets a sliding time window, calculates the in-print evaluation value of the in-print wind evaluation parameter in the sliding time window, updates the collection frequency of the in-print wind evaluation parameter according to the in-print evaluation value, analyzes the in-print evaluation value of each in-print wind evaluation parameter of the risk fault, obtains a fault evaluation value, and determines whether to perform risk fault early warning according to the fault evaluation value; The maintenance personnel scheduling module: constructs a maintenance personnel database, analyzes the selection evaluation value of the maintenance personnel according to the maintenance cases of the maintenance personnel and the current risk fault corresponding to the risk fault early warning, and selects the maintenance personnel corresponding to the maximum selection evaluation value as the task dispatch personnel.

[0028] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of intelligent analysis of print failure data, characterized by: Comprising the following steps: Step one: build the fault parameter library and the fault parameter library, build the risk fault and the scatter diagram of the fault parameter, analyze the correlation between each risk fault in the fault parameter library and each fault parameter in the fault parameter library according to the scatter diagram, obtain the fault parameter information value, and determine the prepress wind evaluation parameter corresponding to each risk fault based on the fault parameter information value; Step two: according to the correlation between the prepress wind evaluation parameter corresponding to the risk fault and the prepress wind evaluation parameter and the risk fault, analyze the wind evaluation value of the risk fault; build the prepress wind evaluation parameter set corresponding to the risk fault, and match the collection frequency of the corresponding prepress wind evaluation parameter set according to the wind evaluation value of the risk fault; Step three: in the printing work, the value of the prepress wind evaluation parameter is collected according to the collection frequency of the prepress wind evaluation parameter set, a sliding time window is set, the prepress evaluation value of the prepress wind evaluation parameter in the sliding time window is calculated, and the collection frequency of the prepress wind evaluation parameter is updated according to the prepress evaluation value; the prepress evaluation value of each prepress wind evaluation parameter of the risk fault is analyzed to obtain the fault evaluation value, and whether to perform risk fault warning is determined according to the fault evaluation value.

2. The method of claim 1, wherein: In step one, the specific process of building the fault parameter library and the fault parameter library is: Collecting raw material data, printing environment data and printing image data in current printing production, and organizing and building a prepress fault parameter library; The raw material data includes: paper thickness, ink fluidity, plate wear resistance; The printing environment data includes: workshop temperature and humidity, cleanliness, air pressure; The printing image data includes: color number, dot distribution density, image fineness; Comb all printing faults caused by prepress raw material data, printing environment data and printing image data, and organize and build a risk fault library.

3. The method of claim 2, wherein: In step one, the correlation between each risk fault in the fault parameter library and each fault parameter in the fault parameter library is analyzed, and the specific process of determining the prepress wind evaluation parameter corresponding to each risk fault based on this analysis is: For each risk fault in the risk fault library, a risk fault and fault parameter rectangular coordinate system is established, in which the risk fault degree is the ordinate and the fault parameter is the abscissa; Then, from the data recorded in the process log of the printing production, the one-to-one corresponding data points of the fault parameter and the risk fault are extracted; Mark these data points one by one in the built rectangular coordinate system to obtain the analysis scatter diagram; Divide the risk of failure into m intervals, denoted as C1, C2, ..., Cn. m Simultaneously, the fault parameters are divided into n intervals, denoted as A1, A2, ..., A n Count the number of data points that fall within the intersection area of ​​each risk failure severity interval and failure parameter interval, and mark them as follows: Where i represents the label of the risk level interval and j represents the label of the fault parameter interval; using the formula: Get the total number of data points in all intersection regions. The joint probability of being in the risk fault level interval Ci and the fault parameter interval Aj The marginal probability of being in the risk failure level range Ci ; and the marginal probability of being in the fault parameter range Aj ; The above , , and Mutual information formula: , get the fault information value MI; Compare the fault parameter information value corresponding to each fault parameter and the risk fault with the corresponding threshold value, if the fault parameter information value is greater than the corresponding threshold value, mark the fault parameter corresponding to the fault parameter information value as the prepress wind evaluation parameter of this risk fault; At the same time, according to the scatter diagram of each fault parameter and risk fault, mark the positive and negative correlation of the fault parameter and risk fault.

4. The method of claim 3, wherein: In step two, the specific process of analyzing the wind evaluation value of the risk fault according to the prepress wind evaluation parameter corresponding to the risk fault is: For each risk failure, the values of the pre-printing wind evaluation parameters corresponding to the risk failure are obtained, the pre-printing wind evaluation parameters positively correlated with the risk failure are marked as , r = 1, 2, …, P; wherein P is the total number of the pre-printing wind evaluation parameters positively correlated with the risk failure; the pre-printing wind evaluation parameters negatively correlated with the risk failure are marked as , a = 1, 2, …, K; wherein K is the total number of the pre-printing wind evaluation parameters negatively correlated with the risk failure; after normalization processing of all the values of the pre-printing wind evaluation parameters, the wind evaluation value FP is obtained by using the formula: , wherein is a preset weight coefficient of the pre-printing wind evaluation parameters positively correlated with the risk failure; is a preset weight coefficient of the pre-printing wind evaluation parameters negatively correlated with the risk failure.

5. The method of claim 4, wherein: In step two, the specific process of building the prepress wind evaluation parameter set corresponding to the risk fault and matching the collection frequency of the corresponding prepress wind evaluation parameter set according to the wind evaluation value of the risk fault is: Sort all risk faults in the risk fault library according to the corresponding wind evaluation value from large to small to obtain a risk fault sorting sequence, and send the risk fault sorting sequence to a monitoring personnel terminal; Each risk fault corresponds to a set of printing wind evaluation parameters; According to the risk fault sorting sequence of the current printing material, each risk fault is matched with the corresponding set of printing wind evaluation parameters of all risk faults in sequence, and the set of printing wind evaluation parameters corresponding to each risk fault is output; For each risk fault, a plurality of wind evaluation value intervals are preset, and each wind evaluation value interval corresponds to a printing wind evaluation parameter collection frequency; the wind evaluation value of the risk fault is matched with all the preset wind evaluation value intervals, and the corresponding printing wind evaluation parameter collection frequency is output, and the printing wind evaluation parameter collection frequency is used as the collection frequency of each wind evaluation parameter in the set of printing wind evaluation parameters of the risk fault.

6. The method of claim 5, wherein: In step three, a sliding time window is set, the printing evaluation value of the printing wind evaluation parameter in the sliding time window is calculated, and the collection frequency of the printing wind evaluation parameter is updated according to the printing evaluation value. The specific process is as follows: In the printing work, for each risk fault, the values of each printing wind evaluation parameter in the set of printing wind evaluation parameters are collected according to the corresponding collection frequency of the set of printing wind evaluation parameters of the risk fault; The values of each printing wind evaluation parameter corresponding to each collection time are obtained; For each printing wind evaluation parameter, a sliding time window is set, the mean, standard deviation and range of the printing wind evaluation parameter in the sliding window are calculated, then the three are assigned different weight coefficients, and the mean, standard deviation and range of the printing wind evaluation parameter are multiplied by the corresponding weight coefficients and then accumulated to obtain the printing evaluation value; A plurality of printing evaluation value sampling frequency update intervals are preset, and the printing evaluation values in the current sliding window and the previous sliding window are continuously compared; Once it is found that the printing evaluation value in the current sliding window jumps to the sampling frequency update interval, the sampling frequency update operation is triggered; Each printing evaluation value sampling frequency update interval corresponds to a printing wind evaluation parameter collection update frequency, and when the sampling frequency update operation is triggered, the collection update frequency corresponding to the sampling frequency update interval in which the printing evaluation value in the current sliding window is located is used as the collection frequency of the printing wind evaluation parameter in the next period of time; The above judgment and collection frequency update process is continuously followed, the printing evaluation values in each sliding time window are continuously calculated, and the interval jump condition is continuously judged. If the interval jump occurs again, the collection frequency is updated again.

7. The method of claim 6, wherein: In step three, the printing evaluation value of each printing wind evaluation parameter of the risk fault is analyzed to obtain a fault evaluation value, and the specific process of judging whether to perform risk fault warning according to the fault evaluation value is as follows: For each printing wind evaluation parameter, a plurality of printing wind evaluation value risk intervals are set, a preset risk value corresponding to each risk interval is set, the printing wind evaluation value corresponding to the current moment of the printing wind evaluation parameter is matched with all the printing wind evaluation value risk intervals, and the corresponding risk value is output ; g = 1, 2, …, H; g is the index of the printing wind evaluation parameter, and H is the total number of the printing wind evaluation parameters The risk evaluation corresponding to each of the printing and wind evaluation parameters corresponding to the risk is substituted into the preset formula model: , to obtain the fault evaluation value GP, wherein is the preset weight coefficient corresponding to the risk evaluation of each printing and wind evaluation parameter; A two-dimensional rectangular coordinate system is established with the fault evaluation value as the vertical coordinate and time as the horizontal coordinate, the value of the risk fault fault evaluation value is calculated in real time, and is substituted into the two-dimensional rectangular coordinate system to obtain a series of fault evaluation data points. The adjacent fault evaluation data points are sequentially connected by a curve to obtain a fault evaluation change curve, a fault evaluation threshold line is preset, and the area surrounded by the part of the fault evaluation change curve that exceeds the fault evaluation threshold line and the fault evaluation threshold line is marked as a wind evaluation area. All wind evaluation areas from the starting moment to the current moment are accumulated to obtain an instant risk accumulation, a preset instant risk accumulation threshold is obtained, if the instant risk accumulation is greater than or equal to the corresponding threshold, the risk failure early warning is triggered; If the risk failure early warning is triggered, the maintenance personnel is dispatched.

8. The method of claim 7, wherein: The specific process of the maintenance personnel dispatching is as follows: A maintenance personnel database is constructed to record personal information, maintenance cases and maintenance scheduling of the maintenance personnel; the personal information includes name, ID card, personnel image and contact information; The maintenance cases store the names of the risk failures that can be maintained by the maintenance personnel, the number of each risk failure processing, the average maintenance time and the maintenance performance score; When the risk failure early warning occurs, all risk failures corresponding to the early warning are obtained, the personnel in the current maintenance scheduling are screened from the maintenance personnel database, the maintenance cases of the personnel are extracted, and the maintenance personnel who have processed all early warning risk failures are screened as the preliminary selected personnel; The selection evaluation value of the maintenance personnel is analyzed, and the maintenance personnel corresponding to the maximum selection evaluation value is selected as the task dispatching personnel to perform the early warning failure operation and maintenance.

9. The method of claim 8, wherein: The specific process of analyzing the selection evaluation value of the maintenance personnel and selecting the maintenance personnel corresponding to the maximum selection evaluation value as the task dispatching personnel to perform the early warning failure operation and maintenance is as follows: For the preliminary selected personnel, the number of processing of each early warning risk failure SLz, the average maintenance time WTz and the maintenance performance score JXz in the maintenance cases of the preliminary selected personnel are comprehensively analyzed, wherein z is the label of the risk failure, z=1, 2, …, D; D is the total number of risk failures; the processing number of risk failures SLz, the average repair time WTz, and the repair performance score JXz are normalized, and the formula is used to obtain the maintenance evaluation value WP, wherein WP=∑b=1D (SLz×WTz×JXz) / (D×SLz×WTz×JXz) is the minimum value of the average repair time of all risk failures , b is a loop variable when the processing number of risk failures is summed, b=1, 2, …, D; , , , respectively, are the weight coefficients of the processing number of risk failures, the average repair time, and the repair performance score. The number of risk failures that have not been processed by the preliminary selected personnel is obtained, the task load RF is obtained by multiplying each risk failure that has not been processed by the average maintenance time of the corresponding risk failure and then adding them up; For each primary candidate, the corresponding evaluation value WP of the primary candidate is normalized with the task load RF to obtain an evaluation value XPZ by using a formula: , wherein d1 and d2 are preset weight coefficients. The preliminary selected personnel with the maximum selection evaluation value is selected from all the preliminary selected personnel as the task dispatching personnel to process the risk failure generated by the early warning; After the task dispatching personnel successfully solves the risk failure, maintenance performance scoring is carried out for each risk failure processed by the task dispatching personnel, and the obtained score is recorded in the maintenance performance score record of the corresponding risk failure in the maintenance case of the personnel.

10. A system for intelligent analysis of printing failure data, applied to the method for intelligent analysis of printing failure data according to any one of claims 1-9, characterized in that: It includes: Fault parameter correlation analysis module: construct fault parameter library and fault parameter library, analyze the correlation between each risk failure in the fault parameter library and each fault parameter in the fault parameter library, and analyze the corresponding pre-press wind evaluation parameters based on the analysis; Wind evaluation assessment and data collection module: analyze the wind evaluation value of the risk failure according to the pre-press wind evaluation parameters corresponding to the risk failure; construct a set of in-press wind evaluation parameters corresponding to the risk failure, and match the collection frequency of the corresponding in-press wind evaluation parameter set according to the wind evaluation value of the risk failure; Evaluation and early warning module: set a sliding time window, calculate the in-press evaluation value of the in-press wind evaluation parameter in the sliding time window, and update the collection frequency of the in-press wind evaluation parameter according to the in-press evaluation value; The in-press evaluation value of each in-press wind evaluation parameter of the risk failure is analyzed to obtain a fault evaluation value, and whether to perform risk failure early warning is determined according to the fault evaluation value; Maintenance personnel dispatching module: construct a maintenance personnel database, analyze the selection evaluation value of the maintenance personnel according to the maintenance cases of the maintenance personnel and the risk failure corresponding to the current risk failure early warning, and select the maintenance personnel corresponding to the maximum selection evaluation value as the task dispatching personnel.

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