CN117726144B - Intelligent digital printing management system and method based on data processing - Google Patents
Intelligent digital printing management system and method based on data processing Download PDFInfo
- Publication number
- CN117726144B CN117726144B CN202410171678.XA CN202410171678A CN117726144B CN 117726144 B CN117726144 B CN 117726144B CN 202410171678 A CN202410171678 A CN 202410171678A CN 117726144 B CN117726144 B CN 117726144B
- Authority
- CN
- China
- Prior art keywords
- printing
- data
- time
- historical
- priority
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007639 printing Methods 0.000 title claims abstract description 733
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 title claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 238000007405 data analysis Methods 0.000 claims abstract description 15
- 238000012544 monitoring process Methods 0.000 claims abstract description 12
- 238000004519 manufacturing process Methods 0.000 claims description 160
- 238000004364 calculation method Methods 0.000 claims description 29
- 238000007726 management method Methods 0.000 claims description 18
- 238000012384 transportation and delivery Methods 0.000 claims description 16
- 238000009826 distribution Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent digital printing management system and method based on data processing, and belongs to the technical field of intelligent digital printing management. The system comprises a data acquisition module, a historical data analysis module, a fault trend analysis module, a real-time monitoring and matching module and a task scheduling and priority updating module; the data acquisition module is responsible for collecting historical printing data to form a historical printing database; the historical data analysis module obtains preset printing priority through historical printing data; the fault trend analysis module acquires historical printing equipment state data and generates a fault trend graph; the real-time monitoring and matching module performs task allocation according to the preset printing priority to obtain a real-time printing equipment data trend graph, and matches the real-time printing equipment data trend graph with the fault trend graph; and the task scheduling and priority updating module adjusts the printing task according to the matching result and updates the printing priority in real time.
Description
Technical Field
The invention relates to the technical field of intelligent digital printing management, in particular to an intelligent digital printing management system and method based on data processing.
Background
The intelligent digital printing is a printing mode based on digital technology and artificial intelligence, electronic texts and images are converted into printed matters in a digital mode, and the intelligent production flow and equipment are utilized to realize efficient and high-quality printing production; the application range of intelligent digital printing is wide, including but not limited to printing production in the fields of books, magazines, newspapers, packaging, advertisements and the like, and is necessary for intelligent digital printing management.
However, in the actual intelligent digital printing process, a situation that different kinds of printed matters need to be printed at the same time is often encountered, and for the situation, the problem that the printing task is distributed unreasonably, so that the printing efficiency is low or the printing task is delayed may occur; in addition, during the printing process, the condition that the printing equipment is out of order is unavoidable, and the shutdown maintenance is usually carried out, but the whole printing flow is interrupted, and the printing time and the printing cost are increased.
Disclosure of Invention
The invention aims to provide an intelligent digital printing management system and method based on data processing, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
an intelligent digital printing management method based on data processing comprises the following steps:
S100, collecting all printing equipment numbers of a printing factory to form a printing equipment set of a printing production line; acquiring historical printing data of a selected time period, and constructing a historical printing database; the historical printing data comprises printing task data and printing equipment state data;
S200, analyzing print task data in the historical print data according to the historical print database, evaluating the historical print priority, calculating the historical print priority score, and obtaining a preset print priority according to the historical print priority score;
s300, acquiring printing equipment state data in the historical printing data, dividing the printing equipment state data in the historical printing data into a plurality of subintervals according to time points of fault records, and obtaining fault trend graphs of all subintervals according to the division of the subintervals;
S400, acquiring real-time printing task data, carrying out printing task allocation by combining preset printing priority, and acquiring real-time printing equipment state data to obtain a real-time printing equipment data trend chart; matching the real-time printing equipment data trend graph with the fault trend graph, and outputting a matching result;
S500, acquiring a matching result, outputting corresponding prompt information by combining with the real-time printing priority score, and adjusting a printing task according to the prompt information; and calculating a real-time printing priority score after the real-time printing task is completed, and updating the preset printing priority.
Further, step S100 includes:
s101, acquiring all printing equipment numbers of a printing factory, and classifying according to the printing production line to obtain a printing equipment set A of all the printing production lines, wherein A= { a1, a2, & gt, an }, a1 represents the printing equipment number of the 1 st printing production line, a2 represents the printing equipment number of the 2 nd printing production line, and the like, and an represents the printing equipment number of the n th printing production line; and n represents the number of the printing production line of the printing factory, and the printing equipment of each printing production line is not unique;
S102, the print job data comprises print label, quantity and print delivery time, and the print job data is expressed as: printed matter label-quantity-printed delivery time; the printed matter label represents the type of printed matter; the printing equipment state data comprises fault records and operation parameters of the printing equipment; the historical printing data are corresponding to the production lines, and are arranged according to the sequence of the starting printing time points of the historical printing data to form a historical printing database, and the printed matter of one printed matter label is printed on one printing production line by default, but the printed matter label of the printed matter on one printing production line is not unique.
Further, step S200 includes:
S201, acquiring a historical printing database, acquiring actual printing completion time T1 corresponding to all printed matter labels according to historical printing data of the same printing production line, and calculating a time difference T according to printing delivery time T0 in the historical printing data, wherein t=T0-T1; default t > 0, which indicates that delivery can be performed on time in actual printing production; obtaining the number M of the prints corresponding to all the print labels according to the print job data, and the maximum value M1 and the minimum value M0 of the number of the prints of different print labels in a selected time period of a production line, and calculating the average value M of the number of the prints of different print labels in the selected time period; the historical printing priority scores S of all printed matter labels of the same printing production line are calculated, and a specific calculation formula is as follows:
，
Wherein, alpha and beta both represent weight parameters, gamma represents error factors, alpha and beta respectively reflect the time difference and the importance degree of the quantity of the printed matters in the process of evaluating the priority, and gamma eliminates the influence of faults of printing equipment on the evaluating priority; t0 represents a time difference threshold, which is the shortest time difference for ensuring that the printed matter can be delivered on time;
S202, according to the historical printing priority scores S, all the printed matter labels of the same printing production line are arranged in the order from big to small according to the historical printing priority scores to obtain a historical printing priority score order set Bn, and Bn= { b1, b2, & gt, bv }, wherein b1 represents the 1 st printed matter label of the nth printing production line, b2 represents the 2 nd printed matter label of the nth printing production line, and similarly, bv represents the v-th printed matter label of the nth printing production line; and v represents the number of different printed matter labels on the nth printing line; according to the recording time point of the historical printing data, a historical printing sequence set C of the printed matter labels of the nth printing production line is obtained, and the sequence of the printed matter labels of the set B and the sequence of the printed matter labels of the set C are compared;
s203, if the sequence of the printed matter labels of the set B and the set C is completely consistent, dividing the printing priority according to the historical printing priority scores in the set B so as to obtain a preset printing priority, wherein one interval corresponds to one preset printing priority, and the number of the intervals represents the number of the preset printing priorities; arranging the intervals in order from small to large, wherein the preset printing priority corresponding to the last interval is the highest;
If the sequence of the printed matter labels in the set B and the set C is not completely consistent, dividing according to the historical printing priority scores in the set B; according to the sequence of the printed matter labels in the set B, printing simulation is carried out by combining the printing task data in the corresponding historical printing data, and the printing simulation is marked as simulated printing task data; calculating a time difference t1 in the simulated printing task data, judging whether t1 is larger than 0, and if all the time differences t1 are larger than 0, obtaining a preset printing priority according to the historical printing priority scores in the set B; if the time difference t1 is smaller than 0, sequentially adjusting the label order of the printed matters in the set C, and only adjusting one label order of the printed matters each time until all the time differences t1 are larger than 0, fitting a calculation formula of the historical printing priority score S according to the adjusted label order of the printed matters of the set C and the historical printing priority score S to obtain alpha 1, beta 1 and gamma 1 respectively, wherein the adjusted calculation formula is as follows:
，
Wherein, alpha 1 and beta 1 both represent adjusted weight parameters, and gamma 1 represents an adjusted error factor; dividing according to the calculation formula of the adjusted historical printing priority score S to obtain the preset printing priority.
The rationality of task allocation can be evaluated by comparing the label sequence set B and the actual printing sequence set C in the historical printing data, if the sequences of the two sets are completely consistent, the task allocation is reasonable, and the preset printing priority is directly divided according to the historical printing priority score; if the sequences of the set B and the set C are not completely consistent, the task allocation is possibly optimized, the sequence after each adjustment and the corresponding historical printing priority score are recorded in the process of adjusting the sequence of the printed matter labels of the set C, and fitting calculation is carried out according to the data to obtain adjusted weight parameters alpha 1, beta 1 and an error factor gamma 1; by adjusting the calculation formula, the print priority can be evaluated more accurately.
Further, step S300 includes:
Acquiring printing equipment state data in printing history data, dividing the printing equipment state data in the printing history data into a plurality of subintervals according to the time points of fault records, wherein the starting point of each divided subinterval is the time point of printing just or the time point of last fault record, and the end point is the time point of next fault record;
And drawing a line graph of the printing equipment operation parameters and time points in the printing equipment state data of all the subintervals in a plane rectangular coordinate system, taking the time points as x-axis and the printing equipment operation parameters as y-axis, and storing the line graph of the printing equipment operation parameters and the time points of all the subintervals as a fault prediction trend graph.
The failure prediction trend graph can more accurately capture the occurrence trend of equipment failure, which is helpful for discovering potential equipment failure in advance and taking corresponding maintenance measures, thereby reducing equipment failure downtime and improving production efficiency; the fault prediction trend chart visually presents the relation between the operation parameters of the printing equipment and the time points, so that an operator can clearly know the change condition of the operation state of the equipment; when the equipment is abnormal, the time point of the fault can be rapidly positioned, so that the fault diagnosis and repair can be rapidly carried out, the downtime of the production line is reduced, and the utilization rate of the equipment is improved.
Further, step S400 includes:
S401, acquiring real-time printing task data, wherein the real-time printing task data comprises printed matter labels, quantity and printing delivery time; finding a corresponding set Bn according to the printed matter labels in the real-time printing task data, and finding a corresponding printing production line according to the value of n;
acquiring historical printing task data of a printing production line, calculating the similarity between the real-time printing task data and the historical printing task data, and selecting a preset printing priority corresponding to the historical printing task data with the largest similarity as the printing priority of the real-time printing task data; acquiring the printing priority of all the real-time printing task data, and distributing the printing tasks according to the arrangement sequence of the printing priority;
S402, performing real-time printing according to a printing task distribution result, and acquiring real-time printing equipment state data aiming at each printing production line, wherein the real-time printing equipment state data comprises fault records and operation parameters of printing equipment; drawing a line graph of the operation parameters of the printing equipment and time points in the printing equipment state data in a plane rectangular coordinate system according to the operation parameters of the printing equipment in the real-time printing equipment state data to obtain a real-time printing equipment data trend graph;
S403, matching the corresponding real-time printing equipment data trend graph with a fault prediction trend graph of the corresponding printing production line in the historical printing data aiming at each printing production line, wherein the matching process is as follows:
obtaining a printing production line number, obtaining a fault prediction trend graph of historical printing data corresponding to the printing production line number, calculating the slope k1 of each data point of the data trend graph of the real-time printing equipment, and calculating the average value k0 of the slope differences of two adjacent data points of the data trend graph of the real-time printing equipment, wherein the calculation formula is as follows:
，
Wherein i represents the data number of the data point slope k1, and 1 to N-1; n represents the number of data point slopes k 1;
For the fault prediction trend graph, calculating the slope R1 of each data point of the fault prediction trend graph, and calculating the average value R0 of the slope differences of two adjacent data points of the fault prediction trend graph, wherein the calculation formula is as follows:
，
Wherein j represents the data number of the data point slope R1, and 1 to M-1; m represents the number of data point slopes R1;
Comparing the magnitude relation between R0 and k0, outputting a matching result, wherein the matching result is as follows: r0=wxk0 and r0+.wxk0, where w represents the coefficient.
By matching the corresponding real-time printing equipment data trend graph with the fault prediction trend graph of the corresponding printing production line in the historical printing data, the running state and fault condition of the printing equipment can be more comprehensively known, and the graphs can be used for further data analysis, such as detecting abnormal fluctuation of equipment running parameters, analyzing equipment performance changes in different time periods and the like, so that accuracy and depth of data analysis can be improved. The fault prediction trend graph can also provide an important reference basis for a production plan, and through analyzing the trend of the running state of the equipment, the possible faults of the equipment in a certain time period in the future can be predicted, so that the production tasks and the equipment maintenance plan are reasonably arranged, the production delay caused by the equipment faults is reduced to the greatest extent, and the production efficiency and the delivery time rate are improved.
Further, step S500 includes:
S501, when R0=wxk0, acquiring the printing equipment numbers of the current printing production line, and finding the printing production line which is nearest to the current production line and contains all the printing equipment numbers of the current printing production line as a temporary printing production line; the printing efficiency of the printing production line can be improved on the premise of ensuring the quality; judging whether the temporary printing production line has a printing task or not, if the temporary printing production line does not have the printing task, outputting the serial number of the current printing production line to related personnel, stopping the printing task of the current printing production line, overhauling printing equipment by the related personnel, and transferring the current printing task to the temporary printing production line for printing; the equipment failure downtime can be reduced, and the equipment utilization rate and the production efficiency are improved.
If the printing task exists, the printing task of the current printing task and the printing task of the temporary printing production line are subjected to printing priority sorting again according to the printing priority, and the current printing task is distributed to the temporary production line according to the sorting result; when R0 is not equal to w x k0, continuing to print the task;
S502, calculating a real-time printing priority score S1 after the real-time printing task is completed, and acquiring a historical printing priority scoring interval Q corresponding to the printing priority of the real-time printing task data in the step S401, wherein Q= [ S_min, S_max ], and calculating an Offset value Offset, wherein the calculation formula is as follows:
，
Wherein μ represents an average value in the interval Q, the magnitude relation between the Offset value Offset and the Offset threshold F is compared, if the ratio of the real-time print job data satisfying the real-time print priority score S1 of Offset is equal to or greater than b ", b represents the ratio threshold, indicating that the real-time print job allocation is reasonable, the preset print priority is not updated; if the ratio of the real-time print job data meeting the real-time print priority score S1 with the Offset less than or equal to F is less than b%, indicating that the real-time print job distribution is unreasonable, outputting prompt information for updating the preset print priority to related personnel, and updating the preset print priority by the related personnel.
An intelligent digital print management system based on data processing, the system comprising: the system comprises a data acquisition module, a historical data analysis module, a fault trend analysis module, a real-time monitoring and matching module and a task scheduling and priority updating module;
The data acquisition module is responsible for collecting the numbers of printing equipment of all printing production lines of a printing factory, classifying the numbers according to the printing types of the printing production lines and forming a printing production line set; acquiring historical printing data of a selected time period, and constructing a historical printing database;
the historical data analysis module analyzes the print task data in the historical print data according to the historical print database, evaluates the historical print priority, calculates the historical print priority score, and obtains the preset print priority according to the historical print priority score;
The fault trend analysis module acquires the state data of the printing equipment in the historical printing data, divides the state data of the printing equipment in the historical printing data into a plurality of subintervals according to the time points of fault records, and obtains a fault trend chart of all subintervals according to the division of the subintervals;
The real-time monitoring and matching module acquires real-time printing task data, performs printing task allocation by combining a preset printing priority, and acquires real-time printing equipment state data to obtain a real-time printing equipment data trend chart; matching the real-time printing equipment data trend graph with the fault trend graph, and outputting a matching result;
the task scheduling and priority updating module obtains a matching result, and outputs corresponding prompt information by combining the real-time printing priority score, and adjusts the printing task according to the prompt information; and calculating a real-time printing priority score after the real-time printing task is completed, and updating the preset printing priority.
Further, the data acquisition module comprises an equipment number collection unit, a classification construction unit and a historical data collection unit;
The equipment number collecting unit is used for collecting the printing equipment numbers of all printing production lines of the printing mill; the classification construction unit classifies the printing types of the printing production lines to form a printing production line set; the method comprises the steps that a historical data collection unit obtains historical printing data of a selected time period, a historical printing database is constructed, and the historical printing data comprises printing task data and printing equipment state data;
the historical data analysis module comprises a printing task analysis unit and a priority calculation unit;
the printing task analysis unit analyzes the printing task data in the historical printing database and evaluates the historical printing priority; the priority calculating unit calculates a preset printing priority according to the score obtained by the historical printing priority evaluation.
Further, the fault trend analysis module comprises a fault record dividing unit and a fault trend graph generating unit;
the fault record dividing unit obtains the state data of the printing equipment in the historical printing database, and divides the state data of the printing equipment in the historical printing data according to the time point of the fault record to obtain a plurality of subintervals; the fault trend graph generating unit generates fault trend graphs of all subintervals according to the division of the subintervals;
the real-time monitoring and matching module comprises a priority distribution unit, a data trend graph generation unit and a matching result output unit;
The priority distribution unit acquires real-time printing task data, and distributes the real-time printing tasks by combining with a preset printing priority score; the data trend graph generating unit acquires real-time printing equipment state data and generates a real-time printing equipment data trend graph according to the real-time printing equipment state data; and the matching result output unit is used for matching the real-time printing equipment data trend graph with the fault trend graph and outputting a matching result.
Further, the task scheduling and priority updating module comprises a prompt information output unit, a task adjusting unit and a real-time priority updating unit;
The prompt information output unit obtains a matching result and outputs corresponding prompt information by combining the real-time printing priority grade; the task adjusting unit adjusts the printing task according to the prompt information; and the real-time priority updating unit calculates the real-time printing priority score after the real-time printing task is completed, and updates the preset printing priority.
Compared with the prior art, the invention has the following beneficial effects: the printing equipment numbers of all printing production lines of a printing factory are collected, and a historical printing database is constructed, so that the comprehensive monitoring and data management of the printing production process are realized; judging whether an optimization space exists or not by comparing task allocation conditions of historical printing data, and correspondingly adjusting to improve the efficiency of a printing production line and the capacity of delivering goods on time, so that the method is beneficial to optimizing the printing production process, improving the resource utilization rate and reducing the production time and cost; priority evaluation and fault trend analysis are carried out by utilizing historical printing data, so that the distribution efficiency of a printing task and the prediction accuracy of equipment faults can be improved; by drawing a fault prediction trend graph, the occurrence trend of equipment faults can be more accurately captured, potential equipment faults can be found in advance, equipment fault downtime is reduced, and production efficiency is improved; by combining the real-time printing task data and the equipment state data, the printing task and the updating printing priority can be timely adjusted, so that the production efficiency and the equipment utilization rate are improved, and the production line is prevented from being excessively busy or idle due to a single printing order.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of an intelligent digital print management system based on data processing according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions:
An intelligent digital print management system based on data processing, the system comprising: the system comprises a data acquisition module, a historical data analysis module, a fault trend analysis module, a real-time monitoring and matching module and a task scheduling and priority updating module;
The data acquisition module is responsible for collecting the numbers of printing equipment of all printing production lines of a printing factory, classifying the numbers according to the printing types of the printing production lines and forming a printing production line set; acquiring historical printing data of a selected time period, and constructing a historical printing database;
the historical data analysis module analyzes the print task data in the historical print data according to the historical print database, evaluates the historical print priority, calculates the historical print priority score, and obtains the preset print priority according to the historical print priority score;
The fault trend analysis module acquires the state data of the printing equipment in the historical printing data, divides the state data of the printing equipment in the historical printing data into a plurality of subintervals according to the time points of fault records, and obtains a fault trend chart of all subintervals according to the division of the subintervals;
The real-time monitoring and matching module acquires real-time printing task data, performs printing task allocation by combining a preset printing priority, and acquires real-time printing equipment state data to obtain a real-time printing equipment data trend chart; matching the real-time printing equipment data trend graph with the fault trend graph, and outputting a matching result;
the task scheduling and priority updating module obtains a matching result, and outputs corresponding prompt information by combining the real-time printing priority score, and adjusts the printing task according to the prompt information; and calculating a real-time printing priority score after the real-time printing task is completed, and updating the preset printing priority.
The data acquisition module comprises an equipment number collection unit, a classification construction unit and a historical data collection unit;
The equipment number collecting unit is used for collecting the printing equipment numbers of all printing production lines of the printing mill; the classification construction unit classifies the printing types of the printing production lines to form a printing production line set; the method comprises the steps that a historical data collection unit obtains historical printing data of a selected time period, a historical printing database is constructed, and the historical printing data comprises printing task data and printing equipment state data;
the historical data analysis module comprises a printing task analysis unit and a priority calculation unit;
the printing task analysis unit analyzes the printing task data in the historical printing database and evaluates the historical printing priority; the priority calculating unit calculates a preset printing priority according to the score obtained by the historical printing priority evaluation.
The fault trend analysis module comprises a fault record dividing unit and a fault trend graph generating unit;
the fault record dividing unit obtains the state data of the printing equipment in the historical printing database, and divides the state data of the printing equipment in the historical printing data according to the time point of the fault record to obtain a plurality of subintervals; the fault trend graph generating unit generates fault trend graphs of all subintervals according to the division of the subintervals;
the real-time monitoring and matching module comprises a priority distribution unit, a data trend graph generation unit and a matching result output unit;
The priority distribution unit acquires real-time printing task data, and distributes the real-time printing tasks by combining with a preset printing priority score; the data trend graph generating unit acquires real-time printing equipment state data and generates a real-time printing equipment data trend graph according to the real-time printing equipment state data; and the matching result output unit is used for matching the real-time printing equipment data trend graph with the fault trend graph and outputting a matching result.
The task scheduling and priority updating module comprises a prompt information output unit, a task adjusting unit and a real-time priority updating unit;
The prompt information output unit obtains a matching result and outputs corresponding prompt information by combining the real-time printing priority grade; the task adjusting unit adjusts the printing task according to the prompt information; and the real-time priority updating unit calculates the real-time printing priority score after the real-time printing task is completed, and updates the preset printing priority.
An intelligent digital printing management method based on data processing comprises the following steps:
S100, collecting all printing equipment numbers of a printing factory to form a printing equipment set of a printing production line; acquiring historical printing data of a selected time period, and constructing a historical printing database; the historical printing data comprises printing task data and printing equipment state data;
S200, analyzing print task data in the historical print data according to the historical print database, evaluating the historical print priority, calculating the historical print priority score, and obtaining a preset print priority according to the historical print priority score;
s300, acquiring printing equipment state data in the historical printing data, dividing the printing equipment state data in the historical printing data into a plurality of subintervals according to time points of fault records, and obtaining fault trend graphs of all subintervals according to the division of the subintervals;
S400, acquiring real-time printing task data, carrying out printing task allocation by combining preset printing priority, and acquiring real-time printing equipment state data to obtain a real-time printing equipment data trend chart; matching the real-time printing equipment data trend graph with the fault trend graph, and outputting a matching result;
S500, acquiring a matching result, outputting corresponding prompt information by combining with the real-time printing priority score, and adjusting a printing task according to the prompt information; and calculating a real-time printing priority score after the real-time printing task is completed, and updating the preset printing priority.
The step S100 includes:
s101, acquiring all printing equipment numbers of a printing factory, and classifying according to the printing production line to obtain a printing equipment set A of all the printing production lines, wherein A= { a1, a2, & gt, an }, a1 represents the printing equipment number of the 1 st printing production line, a2 represents the printing equipment number of the 2 nd printing production line, and the like, and an represents the printing equipment number of the n th printing production line; and n represents the number of the printing production line of the printing factory, and the printing equipment of each printing production line is not unique;
S102, the print job data comprises print label, quantity and print delivery time, and the print job data is expressed as: printed matter label-quantity-printed delivery time; the printed matter label represents the type of printed matter; the printing equipment state data comprises fault records and operation parameters of the printing equipment; the historical printing data are corresponding to the production lines, and are arranged according to the sequence of the starting printing time points of the historical printing data to form a historical printing database, and the printed matter of one printed matter label is printed on one printing production line by default, but the printed matter label of the printed matter on one printing production line is not unique.
Step S200 includes:
S201, acquiring a historical printing database, acquiring actual printing completion time T1 corresponding to all printed matter labels according to historical printing data of the same printing production line, and calculating a time difference T according to printing delivery time T0 in the historical printing data, wherein t=T0-T1; default t > 0, which indicates that delivery can be performed on time in actual printing production; obtaining the number M of the prints corresponding to all the print labels according to the print job data, and the maximum value M1 and the minimum value M0 of the number of the prints of different print labels in a selected time period of a production line, and calculating the average value M of the number of the prints of different print labels in the selected time period; the historical printing priority scores S of all printed matter labels of the same printing production line are calculated, and a specific calculation formula is as follows:
，
Wherein, alpha and beta both represent weight parameters, gamma represents error factors, alpha and beta respectively reflect the time difference and the importance degree of the quantity of the printed matters in the process of evaluating the priority, and gamma eliminates the influence of faults of printing equipment on the evaluating priority; t0 represents a time difference threshold, which is the shortest time difference for ensuring that the printed matter can be delivered on time;
S202, according to the historical printing priority scores S, all the printed matter labels of the same printing production line are arranged in the order from big to small according to the historical printing priority scores to obtain a historical printing priority score order set Bn, and Bn= { b1, b2, & gt, bv }, wherein b1 represents the 1 st printed matter label of the nth printing production line, b2 represents the 2 nd printed matter label of the nth printing production line, and similarly, bv represents the v-th printed matter label of the nth printing production line; and v represents the number of different printed matter labels on the nth printing line; according to the recording time point of the historical printing data, a historical printing sequence set C of the printed matter labels of the nth printing production line is obtained, and the sequence of the printed matter labels of the set B and the sequence of the printed matter labels of the set C are compared;
s203, if the sequence of the printed matter labels of the set B and the set C is completely consistent, dividing the printing priority according to the historical printing priority scores in the set B so as to obtain a preset printing priority, wherein one interval corresponds to one preset printing priority, and the number of the intervals represents the number of the preset printing priorities; arranging the intervals in order from small to large, wherein the preset printing priority corresponding to the last interval is the highest;
If the sequence of the printed matter labels in the set B and the set C is not completely consistent, dividing according to the historical printing priority scores in the set B; according to the sequence of the printed matter labels in the set B, printing simulation is carried out by combining the printing task data in the corresponding historical printing data, and the printing simulation is marked as simulated printing task data; calculating a time difference t1 in the simulated printing task data, judging whether t1 is larger than 0, and if all the time differences t1 are larger than 0, obtaining a preset printing priority according to the historical printing priority scores in the set B; if the time difference t1 is smaller than 0, sequentially adjusting the label order of the printed matters in the set C, and only adjusting one label order of the printed matters each time until all the time differences t1 are larger than 0, fitting a calculation formula of the historical printing priority score S according to the adjusted label order of the printed matters of the set C and the historical printing priority score S to obtain alpha 1, beta 1 and gamma 1 respectively, wherein the adjusted calculation formula is as follows:
，
Wherein, alpha 1 and beta 1 both represent adjusted weight parameters, and gamma 1 represents an adjusted error factor; dividing according to the calculation formula of the adjusted historical printing priority score S to obtain the preset printing priority.
The rationality of task allocation can be evaluated by comparing the label sequence set B and the actual printing sequence set C in the historical printing data, if the sequences of the two sets are completely consistent, the task allocation is reasonable, and the preset printing priority can be directly divided according to the historical printing priority score; if the sequences of the set B and the set C are not completely consistent, the task allocation is possibly optimized, the sequence after each adjustment and the corresponding historical printing priority score are recorded in the process of adjusting the sequence of the printed matter labels of the set C, and fitting calculation is carried out according to the data to obtain adjusted weight parameters alpha 1, beta 1 and an error factor gamma 1; by adjusting the calculation formula, the print priority can be evaluated more accurately.
In this embodiment, it is assumed that the historical print priority scores S of all the print labels of a certain printing line are respectively: s1, s2, s3,..sx, wherein s1 is the maximum value of the historical print priority score, sx is the minimum value of the historical print priority score, and the historical print priority scores between s1 and sx are sequentially reduced, assuming that the preset priority has 5 levels, A, B, C, D and E respectively;
The calculation process of the historical printing priority scoring interval Q aiming at 5 preset priorities is as follows:
calculating the class division gradient Δs, i.e., Δs= [ s1-sx/5], then the division interval for each class is:
The preset priority a is [ sx, sx+Δs ], the preset priority B is (sx+Δs, sx+2 Δs ], the preset priority C is (sx+2 Δs, sx+3 Δs ], the preset priority D is (sx+3 Δs, sx+4 Δs ], and the preset priority E is (sx+4 Δs, sx+5 Δs ].
Step S300 includes:
Acquiring printing equipment state data in printing history data, dividing the printing equipment state data in the printing history data into a plurality of subintervals according to the time points of fault records, wherein the starting point of each divided subinterval is the time point of printing just or the time point of last fault record, and the end point is the time point of next fault record;
And drawing a line graph of the printing equipment operation parameters and time points in the printing equipment state data of all the subintervals in a plane rectangular coordinate system, taking the time points as x-axis and the printing equipment operation parameters as y-axis, and storing the line graph of the printing equipment operation parameters and the time points of all the subintervals as a fault prediction trend graph.
The failure prediction trend graph can more accurately capture the occurrence trend of equipment failure, which is helpful for discovering potential equipment failure in advance and taking corresponding maintenance measures, thereby reducing equipment failure downtime and improving production efficiency; the fault prediction trend chart visually presents the relation between the operation parameters of the printing equipment and the time points, so that an operator can clearly know the change condition of the operation state of the equipment; when the equipment is abnormal, the time point of the fault can be rapidly positioned, so that the fault diagnosis and repair can be rapidly carried out, the downtime of the production line is reduced, and the utilization rate of the equipment is improved.
Step S400 includes:
S401, acquiring real-time printing task data, wherein the real-time printing task data comprises printed matter labels, quantity and printing delivery time; finding a corresponding set Bn according to the printed matter labels in the real-time printing task data, and finding a corresponding printing production line according to the value of n;
acquiring historical printing task data of a printing production line, calculating the similarity between the real-time printing task data and the historical printing task data, and selecting a preset printing priority corresponding to the historical printing task data with the largest similarity as the printing priority of the real-time printing task data; acquiring the printing priority of all the real-time printing task data, and distributing the printing tasks according to the arrangement sequence of the printing priority;
S402, performing real-time printing according to a printing task distribution result, and acquiring real-time printing equipment state data aiming at each printing production line, wherein the real-time printing equipment state data comprises fault records and operation parameters of printing equipment; drawing a line graph of the operation parameters of the printing equipment and time points in the printing equipment state data in a plane rectangular coordinate system according to the operation parameters of the printing equipment in the real-time printing equipment state data to obtain a real-time printing equipment data trend graph;
S403, matching the corresponding real-time printing equipment data trend graph with a fault prediction trend graph of the corresponding printing production line in the historical printing data aiming at each printing production line, wherein the matching process is as follows:
obtaining a printing production line number, obtaining a fault prediction trend graph of historical printing data corresponding to the printing production line number, calculating the slope k1 of each data point of the data trend graph of the real-time printing equipment, and calculating the average value k0 of the slope differences of two adjacent data points of the data trend graph of the real-time printing equipment, wherein the calculation formula is as follows:
，
Wherein i represents the data number of the data point slope k1, and 1 to N-1; n represents the number of data point slopes k 1;
For the fault prediction trend graph, calculating the slope R1 of each data point of the fault prediction trend graph, and calculating the average value R0 of the slope differences of two adjacent data points of the fault prediction trend graph, wherein the calculation formula is as follows:
，
Wherein j represents the data number of the data point slope R1, and 1 to M-1; m represents the number of data point slopes R1;
Comparing the magnitude relation between R0 and k0, outputting a matching result, wherein the matching result is as follows: r0=wxk0 and r0+.wxk0, where w represents the coefficient.
By matching the corresponding real-time printing equipment data trend graph with the fault prediction trend graph of the corresponding printing production line in the historical printing data, the running state and fault condition of the printing equipment can be more comprehensively known, and the graphs can be used for further data analysis, such as detecting abnormal fluctuation of equipment running parameters, analyzing equipment performance changes in different time periods and the like, so that accuracy and depth of data analysis can be improved. The fault prediction trend graph can also provide an important reference basis for a production plan, and through analyzing the trend of the running state of the equipment, the possible faults of the equipment in a certain time period in the future can be predicted, so that the production tasks and the equipment maintenance plan are reasonably arranged, the production delay caused by the equipment faults is reduced to the greatest extent, and the production efficiency and the delivery time rate are improved.
Step S500 includes:
S501, when R0=wxk0, acquiring the printing equipment numbers of the current printing production line, and finding the printing production line which is nearest to the current production line and contains all the printing equipment numbers of the current printing production line as a temporary printing production line; the printing efficiency of the printing production line can be improved on the premise of ensuring the quality; judging whether the temporary printing production line has a printing task or not, if the temporary printing production line does not have the printing task, outputting the serial number of the current printing production line to related personnel, stopping the printing task of the current printing production line, overhauling printing equipment by the related personnel, and transferring the current printing task to the temporary printing production line for printing; the equipment failure downtime can be reduced, and the equipment utilization rate and the production efficiency are improved.
If the printing task exists, the printing task of the current printing task and the printing task of the temporary printing production line are subjected to printing priority sorting again according to the printing priority, and the current printing task is distributed to the temporary production line according to the sorting result; when R0 is not equal to w x k0, continuing to print the task;
S502, calculating a real-time printing priority score S1 after the real-time printing task is completed, and acquiring a historical printing priority scoring interval Q corresponding to the printing priority of the real-time printing task data in the step S401, wherein Q= [ S_min, S_max ], and calculating an Offset value Offset, wherein the calculation formula is as follows:
，
Wherein μ represents an average value in the interval Q, the magnitude relation between the Offset value Offset and the Offset threshold F is compared, if the ratio of the real-time print job data satisfying the real-time print priority score S1 of Offset is equal to or greater than b ", b represents the ratio threshold, indicating that the real-time print job allocation is reasonable, the preset print priority is not updated; if the ratio of the real-time print job data meeting the real-time print priority score S1 with the Offset less than or equal to F is less than b%, indicating that the real-time print job distribution is unreasonable, outputting prompt information for updating the preset print priority to related personnel, and updating the preset print priority by the related personnel.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An intelligent digital printing management method based on data processing is characterized in that: the method comprises the following steps:
S100, collecting all printing equipment numbers of a printing factory to form a printing equipment set of a printing production line; acquiring historical printing data of a selected time period, and constructing a historical printing database; the historical printing data comprises printing task data and printing equipment state data;
S200, analyzing print task data in the historical print data according to the historical print database, evaluating the historical print priority, calculating the historical print priority score, and obtaining a preset print priority according to the historical print priority score;
The step S200 includes:
S201, acquiring a historical printing database, acquiring actual printing completion time T1 corresponding to all printed matter labels according to historical printing data of the same printing production line, and calculating a time difference T according to printing delivery time T0 in the historical printing data, wherein t=T0-T1; obtaining the number M of the prints corresponding to all the print labels according to the print job data, and the maximum value M1 and the minimum value M0 of the number of the prints of different print labels in a selected time period of a production line, and calculating the average value M of the number of the prints of different print labels in the selected time period; the historical printing priority scores S of all printed matter labels of the same printing production line are calculated, and a specific calculation formula is as follows: Wherein, alpha and beta both represent weight parameters, gamma represents an error factor, and t0 represents a time difference threshold;
S202, according to the historical printing priority scores S, all the printed matter labels of the same printing production line are arranged in the order from big to small according to the historical printing priority scores to obtain a historical printing priority score order set Bn, and Bn= { b1, b2, & gt, bv }, wherein b1 represents the 1 st printed matter label of the nth printing production line, b2 represents the 2 nd printed matter label of the nth printing production line, and similarly, bv represents the v-th printed matter label of the nth printing production line; and v represents the number of different printed matter labels on the nth printing line; according to the recording time point of the historical printing data, a historical printing sequence set C of the printed matter labels of the nth printing production line is obtained, and the sequence of the printed matter labels of the set B and the sequence of the printed matter labels of the set C are compared;
s203, if the sequence of the printed matter labels of the set B and the set C is completely consistent, dividing the printing priority according to the historical printing priority scores in the set B so as to obtain a preset printing priority, wherein one interval corresponds to one preset printing priority, and the number of the intervals represents the number of the preset printing priorities; arranging the intervals in order from small to large, wherein the preset printing priority corresponding to the last interval is the highest;
If the sequence of the printed matter labels in the set B and the set C is not completely consistent, dividing according to the historical printing priority scores in the set B; according to the sequence of the printed matter labels in the set B, printing simulation is carried out by combining the printing task data in the corresponding historical printing data, and the printing simulation is marked as simulated printing task data; calculating a time difference t1 in the simulated printing task data, judging whether t1 is larger than 0, and if all the time differences t1 are larger than 0, obtaining a preset printing priority according to the historical printing priority scores in the set B; if the time difference t1 is smaller than 0, sequentially adjusting the label order of the printed matters in the set C, and only adjusting one label order of the printed matters each time until all the time differences t1 are larger than 0, fitting a calculation formula of the historical printing priority score S according to the adjusted label order of the printed matters of the set C and the historical printing priority score S to obtain alpha 1, beta 1 and gamma 1 respectively, wherein the adjusted calculation formula is as follows: ，
Wherein, alpha 1 and beta 1 both represent adjusted weight parameters, and gamma 1 represents an adjusted error factor; dividing according to a calculation formula of the adjusted historical printing priority score S to obtain a preset printing priority;
s300, acquiring printing equipment state data in the historical printing data, dividing the printing equipment state data in the historical printing data into a plurality of subintervals according to time points of fault records, and obtaining fault trend graphs of all subintervals according to the division of the subintervals;
S400, acquiring real-time printing task data, carrying out printing task allocation by combining preset printing priority, and acquiring real-time printing equipment state data to obtain a real-time printing equipment data trend chart; matching the real-time printing equipment data trend graph with the fault trend graph, and outputting a matching result;
The step S400 includes:
S401, acquiring real-time printing task data, wherein the real-time printing task data comprises printed matter labels, quantity and printing delivery time; finding a corresponding set Bn according to the printed matter labels in the real-time printing task data, and finding a corresponding printing production line according to the value of n;
acquiring historical printing task data of a printing production line, calculating the similarity between the real-time printing task data and the historical printing task data, and selecting a preset printing priority corresponding to the historical printing task data with the largest similarity as the printing priority of the real-time printing task data; acquiring the printing priority of all the real-time printing task data, and distributing the printing tasks according to the arrangement sequence of the printing priority;
S402, performing real-time printing according to a printing task distribution result, and acquiring real-time printing equipment state data aiming at each printing production line, wherein the real-time printing equipment state data comprises fault records and operation parameters of printing equipment; drawing a line graph of the operation parameters of the printing equipment and time points in the printing equipment state data in a plane rectangular coordinate system according to the operation parameters of the printing equipment in the real-time printing equipment state data to obtain a real-time printing equipment data trend graph;
S403, matching the corresponding real-time printing equipment data trend graph with a fault prediction trend graph of the corresponding printing production line in the historical printing data aiming at each printing production line, wherein the matching process is as follows:
obtaining a printing production line number, obtaining a fault prediction trend graph of historical printing data corresponding to the printing production line number, calculating the slope k1 of each data point of the data trend graph of the real-time printing equipment, and calculating the average value k0 of the slope differences of two adjacent data points of the data trend graph of the real-time printing equipment, wherein the calculation formula is as follows: wherein i represents the data number of the data point slope k1, and 1 to N-1; n represents the number of data point slopes k 1;
For the fault prediction trend graph, calculating the slope R1 of each data point of the fault prediction trend graph, and calculating the average value R0 of the slope differences of two adjacent data points of the fault prediction trend graph, wherein the calculation formula is as follows: wherein j represents the data number of the data point slope R1, and 1 to M-1; m represents the number of data point slopes R1;
comparing the magnitude relation between R0 and k0, outputting a matching result, wherein the matching result is as follows: r0=wxk0 and r0++wxk0, where w represents a coefficient;
S500, acquiring a matching result, outputting corresponding prompt information by combining with the real-time printing priority score, and adjusting a printing task according to the prompt information; calculating a real-time printing priority score after the real-time printing task is completed, and updating a preset printing priority;
The step S500 includes:
s501, when R0=wxk0, acquiring the printing equipment numbers of the current printing production line, and finding the printing production line which is nearest to the current production line and contains all the printing equipment numbers of the current printing production line as a temporary printing production line; judging whether the temporary printing production line has a printing task or not, if the temporary printing production line does not have the printing task, outputting the serial number of the current printing production line to related personnel, stopping the printing task of the current printing production line, overhauling printing equipment by the related personnel, and transferring the current printing task to the temporary printing production line for printing; if the printing task exists, the printing task of the current printing task and the printing task of the temporary printing production line are subjected to printing priority sorting again according to the printing priority, and the current printing task is distributed to the temporary production line according to the sorting result; when R0 is not equal to w x k0, continuing to print the task;
S502, calculating a real-time printing priority score S1 after the real-time printing task is completed, and acquiring a historical printing priority scoring interval Q corresponding to the printing priority of the real-time printing task data in the step S401, wherein Q= [ S_min, S_max ], and calculating an Offset value Offset, wherein the calculation formula is as follows: Wherein μ represents an average value in the interval Q, the magnitude relation between the Offset value Offset and the Offset threshold F is compared, and if the ratio of the real-time print job data satisfying the real-time print priority score S1 of Offset is equal to or greater than b ", b represents the ratio threshold, the preset print priority is not updated; if the ratio of the real-time print job data meeting the real-time print priority score S1 with the Offset less than or equal to F is less than b%, outputting prompt information for updating the preset print priority to related personnel, and updating the preset print priority by the related personnel.
2. The intelligent digital printing management method based on data processing according to claim 1, wherein: the step S100 includes:
s101, acquiring all printing equipment numbers of a printing factory, and classifying according to the printing production line to obtain a printing equipment set A of all the printing production lines, wherein A= { a1, a2, & gt, an }, a1 represents the printing equipment number of the 1 st printing production line, a2 represents the printing equipment number of the 2 nd printing production line, and the like, and an represents the printing equipment number of the n th printing production line; and n represents the number of the printing production line of the printing factory, and the printing equipment of each printing production line is not unique;
S102, the print job data comprises print label, quantity and print delivery time, and the print job data is expressed as: printed matter label-quantity-printed delivery time; the printed matter label represents the type of printed matter; the printing equipment state data comprises fault records and operation parameters of the printing equipment; and the historical printing data are corresponding to the production line, and are arranged according to the sequence of the starting printing time points of the historical printing data to form a historical printing database.
3. The intelligent digital printing management method based on data processing according to claim 1, wherein: the step S300 includes:
Acquiring printing equipment state data in printing history data, dividing the printing equipment state data in the printing history data into a plurality of subintervals according to the time points of fault records, wherein the starting point of each divided subinterval is the time point of printing just or the time point of last fault record, and the end point is the time point of next fault record;
And drawing a line graph of the printing equipment operation parameters and time points in the printing equipment state data of all the subintervals in a plane rectangular coordinate system, taking the time points as x-axis and the printing equipment operation parameters as y-axis, and storing the line graph of the printing equipment operation parameters and the time points of all the subintervals as a fault prediction trend graph.
4. An intelligent digital printing management system based on data processing, which is applied to the intelligent digital printing management method based on data processing as set forth in any one of claims 1-3, and is characterized in that: the system comprises: the system comprises a data acquisition module, a historical data analysis module, a fault trend analysis module, a real-time monitoring and matching module and a task scheduling and priority updating module;
The data acquisition module is responsible for collecting the printing equipment numbers of all printing production lines of a printing factory, and classifying the printing equipment numbers according to the printing production lines to form a printing production line set; acquiring historical printing data of a selected time period, and constructing a historical printing database;
The historical data analysis module analyzes the print task data in the historical print data according to the historical print database, evaluates the historical print priority, calculates the historical print priority score, and obtains the preset print priority according to the historical print priority score;
The fault trend analysis module acquires the state data of the printing equipment in the historical printing data, divides the state data of the printing equipment in the historical printing data into a plurality of subintervals according to the time points of fault records, and obtains fault trend graphs of all subintervals according to the division of the subintervals;
The real-time monitoring and matching module acquires real-time printing task data, performs printing task allocation by combining a preset printing priority, and acquires real-time printing equipment state data to obtain a real-time printing equipment data trend chart; matching the real-time printing equipment data trend graph with the fault trend graph, and outputting a matching result;
the task scheduling and priority updating module acquires a matching result, outputs corresponding prompt information by combining the real-time printing priority score, and adjusts the printing task according to the prompt information; and calculating a real-time printing priority score after the real-time printing task is completed, and updating the preset printing priority.
5. The intelligent digital print management system based on data processing of claim 4, wherein: the data acquisition module comprises an equipment number collection unit, a classification construction unit and a historical data collection unit;
the equipment number collecting unit is responsible for collecting the printing equipment numbers of a printing factory; the classification construction unit classifies the printing production lines to form a printing production line set; the historical data collection unit acquires historical printing data of a selected time period, a historical printing database is constructed, and the historical printing data comprises printing task data and printing equipment state data;
The historical data analysis module comprises a printing task analysis unit and a priority calculation unit;
the printing task analysis unit analyzes the printing task data in the historical printing database and evaluates the historical printing priority; the priority calculating unit calculates a preset printing priority according to the score obtained by the historical printing priority evaluation.
6. The intelligent digital print management system based on data processing of claim 4, wherein: the fault trend analysis module comprises a fault record dividing unit and a fault trend graph generating unit;
The fault record dividing unit acquires the state data of the printing equipment in the historical printing database, and divides the state data of the printing equipment in the historical printing data according to the time point of the fault record to obtain a plurality of subintervals; the fault trend graph generating unit generates fault trend graphs of all subintervals according to the division of the subintervals;
The real-time monitoring and matching module comprises a priority distribution unit, a data trend graph generation unit and a matching result output unit;
The priority distribution unit acquires real-time printing task data, and distributes the real-time printing tasks by combining with a preset printing priority score; the data trend graph generating unit acquires real-time printing equipment state data and generates a real-time printing equipment data trend graph according to the real-time printing equipment state data; and the matching result output unit is used for matching the data trend graph of the real-time printing equipment with the fault trend graph and outputting a matching result.
7. The intelligent digital print management system based on data processing of claim 4, wherein: the task scheduling and priority updating module comprises a prompt information output unit, a task adjusting unit and a real-time priority updating unit;
The prompt information output unit obtains a matching result and outputs corresponding prompt information by combining the real-time printing priority grade; the task adjusting unit adjusts the printing task according to the prompt information; and the real-time priority updating unit calculates the real-time printing priority score after the real-time printing task is completed, and updates the preset printing priority.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410171678.XA CN117726144B (en) | 2024-02-07 | 2024-02-07 | Intelligent digital printing management system and method based on data processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410171678.XA CN117726144B (en) | 2024-02-07 | 2024-02-07 | Intelligent digital printing management system and method based on data processing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117726144A CN117726144A (en) | 2024-03-19 |
| CN117726144B true CN117726144B (en) | 2024-05-07 |
Family
ID=90200128
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410171678.XA Active CN117726144B (en) | 2024-02-07 | 2024-02-07 | Intelligent digital printing management system and method based on data processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117726144B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2635954A1 (en) * | 2005-12-30 | 2007-07-19 | Steven Kays | Genius adaptive design |
| CN104476930A (en) * | 2014-12-12 | 2015-04-01 | 中国科学院自动化研究所 | Large-scale distributed digital ink jet printing control device |
| DE102018004923A1 (en) * | 2017-06-27 | 2018-12-27 | Fanuc Corporation | Error predictor and machinelle learning device |
| EP3483799A1 (en) * | 2017-11-10 | 2019-05-15 | General Electric Company | Systems and methods to generate an asset workscope |
| EP3483798A1 (en) * | 2017-11-10 | 2019-05-15 | General Electric Company | Methods and apparatus to generate an optimized workscope |
| CN109829245A (en) * | 2019-02-25 | 2019-05-31 | 中科诺维(北京)科技有限公司 | Bearing fault method for early warning and device |
| CN110163436A (en) * | 2019-05-23 | 2019-08-23 | 西北工业大学 | Intelligent workshop production optimization method based on bottleneck prediction |
| CN110458384A (en) * | 2019-06-21 | 2019-11-15 | 珠海格力电器股份有限公司 | Printed matter method for allocating tasks and system |
| CN113077172A (en) * | 2021-04-19 | 2021-07-06 | 瑞湖智科数据(苏州)有限公司 | Equipment state trend analysis and fault diagnosis method |
| CN113971087A (en) * | 2020-07-23 | 2022-01-25 | 同方威视技术股份有限公司 | Task allocation method, device and system and computer storage medium |
| CN115239202A (en) * | 2022-09-15 | 2022-10-25 | 建筑材料工业信息中心 | Production management method and system of assembly type component based on historical data |
| CA3177620A1 (en) * | 2021-05-06 | 2022-11-06 | Strong Force Iot Portfolio 2016, Llc | Quantum, biological, computer vision, and neural network systems for industrial internet of things |
| JP2023111111A (en) * | 2022-01-31 | 2023-08-10 | 株式会社グーフ | Information processing system, method, and apparatus |
| CN117114576A (en) * | 2023-08-31 | 2023-11-24 | 佛山市太米智能科技有限公司 | Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence |
| CN117207664A (en) * | 2023-09-26 | 2023-12-12 | 中国印钞造币集团有限公司 | Intelligent diagnosis and operation and maintenance method and system for printing equipment and readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9417829B2 (en) * | 2012-09-26 | 2016-08-16 | Electronics For Imaging, Inc. | Production planning and monitoring in inkjet devices |
-
2024
- 2024-02-07 CN CN202410171678.XA patent/CN117726144B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2635954A1 (en) * | 2005-12-30 | 2007-07-19 | Steven Kays | Genius adaptive design |
| CN104476930A (en) * | 2014-12-12 | 2015-04-01 | 中国科学院自动化研究所 | Large-scale distributed digital ink jet printing control device |
| DE102018004923A1 (en) * | 2017-06-27 | 2018-12-27 | Fanuc Corporation | Error predictor and machinelle learning device |
| EP3483799A1 (en) * | 2017-11-10 | 2019-05-15 | General Electric Company | Systems and methods to generate an asset workscope |
| EP3483798A1 (en) * | 2017-11-10 | 2019-05-15 | General Electric Company | Methods and apparatus to generate an optimized workscope |
| CN109829245A (en) * | 2019-02-25 | 2019-05-31 | 中科诺维(北京)科技有限公司 | Bearing fault method for early warning and device |
| CN110163436A (en) * | 2019-05-23 | 2019-08-23 | 西北工业大学 | Intelligent workshop production optimization method based on bottleneck prediction |
| CN110458384A (en) * | 2019-06-21 | 2019-11-15 | 珠海格力电器股份有限公司 | Printed matter method for allocating tasks and system |
| CN113971087A (en) * | 2020-07-23 | 2022-01-25 | 同方威视技术股份有限公司 | Task allocation method, device and system and computer storage medium |
| CN113077172A (en) * | 2021-04-19 | 2021-07-06 | 瑞湖智科数据(苏州)有限公司 | Equipment state trend analysis and fault diagnosis method |
| CA3177620A1 (en) * | 2021-05-06 | 2022-11-06 | Strong Force Iot Portfolio 2016, Llc | Quantum, biological, computer vision, and neural network systems for industrial internet of things |
| JP2023111111A (en) * | 2022-01-31 | 2023-08-10 | 株式会社グーフ | Information processing system, method, and apparatus |
| CN115239202A (en) * | 2022-09-15 | 2022-10-25 | 建筑材料工业信息中心 | Production management method and system of assembly type component based on historical data |
| CN117114576A (en) * | 2023-08-31 | 2023-11-24 | 佛山市太米智能科技有限公司 | Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence |
| CN117207664A (en) * | 2023-09-26 | 2023-12-12 | 中国印钞造币集团有限公司 | Intelligent diagnosis and operation and maintenance method and system for printing equipment and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 无量纲指标趋势分析法在风电机组齿轮箱故障预警诊断中的应用;顾煜炯;贾子文;尹传涛;任玉亭;;振动与冲击;20171015(第19期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117726144A (en) | 2024-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021052031A1 (en) | Statistical interquartile range-based commodity inventory risk early warning method and system, and computer readable storage medium | |
| US9678502B2 (en) | Process control of an industrial plant | |
| EP1659527A1 (en) | Method of and system for representing unscheduled events in a service plan | |
| JP4928962B2 (en) | How to predict call volume | |
| CN102498445A (en) | Supervised fault learning using rule-generated samples for machine condition monitoring | |
| CN116342073B (en) | Book printing digital information management system and method thereof | |
| CN112550059B (en) | Intelligent electricity changing method and system based on big data and sharing bicycle cloud platform | |
| CN109507992B (en) | Method, device and equipment for predicting faults of locomotive brake system components | |
| CN116130076B (en) | Medical equipment information management system based on cloud platform | |
| CN117726144B (en) | Intelligent digital printing management system and method based on data processing | |
| CN116051037A (en) | Project progress supervision system based on data analysis | |
| CN115936676A (en) | Equipment health management method and system based on digital twins | |
| JP2009289056A (en) | Device for supporting personnel arrangement planning | |
| CN110991938B (en) | Energy consumption calculation method for multi-product production line | |
| CN114819652B (en) | Intelligent fixed asset management method and system based on block chain | |
| Girod et al. | The evaluation of productive efficiency using a fuzzy mathematical programming approach: the case of the newspaper preprint insertion process | |
| CN117195451A (en) | Bridge monitoring data restoration method based on graph theory | |
| CN116452150A (en) | Project information collaborative management system | |
| CN116050788A (en) | Industrial intelligent scheduling management system | |
| CN111338788B (en) | Resource quota approval method and storage medium | |
| CN117522418B (en) | Student information data management system and method based on SaaS mode | |
| CN112712283B (en) | Real-time order separating method and system | |
| CN114297255B (en) | Network quality work order fault early warning method based on log analysis | |
| CN116797001B (en) | Management system and method for financial industry practitioners | |
| CN117421690B (en) | Intelligent park energy consumption real-time monitoring method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |