Intelligent printing management method and system based on document weight
Through an intelligent printing management method based on document weight, the LSTM model is used to predict printing demand, dynamically adjust the number of equipment, and prioritize printing tasks according to multiple factors, the problem of inefficiency of traditional printing management is solved, and efficient resource utilization and customer satisfaction are achieved.
Patent Information
- Application Number
- CN202510085616.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-20
- Publication Date
- 2025-05-06
AI Technical Summary
Traditional printing management methods are inefficient and difficult to cope with changing printing needs, especially in large enterprises or print service providers, resulting in waste of resources, overload of equipment and reduced customer satisfaction.
Using an intelligent printing management method based on document weight, we predict the number of future printed pages through the LSTM neural network model, set up a quantity threshold mechanism to dynamically adjust the number of printing equipment, and prioritize the printing tasks according to factors such as profit margin and customer importance through the scheduling model.
It realizes accurate prediction of the number of pages printed in the future, dynamically adjusts equipment configuration, improves resource utilization and operational efficiency, ensures priority handling of emergency and important tasks, and improves customer satisfaction.
Smart Images
Figure CN119937953A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of priority scheduling, and in particular relates to an intelligent printing management method and system based on document weight. Background Art
[0002] In current industrial printing, print management is an important part of daily operations, and its efficiency and cost control are directly related to the company's operating costs and customer satisfaction. Traditional print management methods mostly rely on manual prediction of printing needs, manual assignment of printing tasks, and static configuration of printing equipment. This method is not only inefficient, but also difficult to cope with changing printing needs, especially in large enterprises or print service providers.
[0003] Traditional printing demand forecasting is usually based on simple statistical methods, such as moving average method or time series analysis. These methods are not effective in dealing with nonlinear and dynamically changing printing demand, and it is difficult to accurately predict the number of pages to be printed in the future. In addition, in terms of printing task scheduling, there is often a lack of scientific priority sorting mechanism, which may cause urgent or important printing tasks to be delayed due to waiting in line, affecting the timeliness of business processing and customer satisfaction.
[0004] In addition, the configuration of printing equipment is usually based on experience or fixed rules, which fails to fully utilize equipment resources, resulting in resource waste or equipment overload, affecting printing efficiency and equipment life. Therefore, it is particularly important to develop an intelligent printing management system that can intelligently predict printing needs, dynamically adjust printing equipment configuration, and effectively manage printing task priorities. Summary of the invention
[0005] In order to solve the above problems existing in the prior art, the present invention provides an intelligent printing management method and system based on document weight. The purpose of the present invention can be achieved through the following technical solutions: Obtaining order information, order requirement information, and the number of pages of a file to be printed, wherein the order requirement information includes material specifications, printing quantity, and printing requirements; Classifying the files to be printed according to the material specifications and the printing requirements to obtain classified files, counting the classified files according to the printing quantity and the number of file pages to obtain the total number of classified files, adding a timestamp according to the total number of classified files to obtain the total number of time-series classified files, storing the total number of time-series classified files to obtain a time-series total number of pages sequence, and calculating the predicted number according to the time-series total number of pages sequence through a prediction model; Preset a quantity threshold, divide the predicted quantity according to the quantity threshold to obtain a printing device startup quantity, calculate the total weight and total volume of the classified documents according to the material specifications and the total number of pages of the classified documents, obtain a logistics quotation, calculate the logistics cost according to the logistics quotation, the total weight, and the total volume, obtain a material unit cost price, and calculate the printing cost according to the material unit cost price and the total number of pages of the classified documents; The number of the printing device is calculated by the scheduling model according to the printing cost, the logistics cost, the order information, and the number of devices started.
[0006] Specifically, the order information includes delivery date, customer importance, and transaction price; the material specifications include material type and material weight, and the material types include but are not limited to A0 paper, A1 paper, A2 paper, A3 paper, and A4 paper; the printing requirements include but are not limited to black and white printing and color printing.
[0007] Specifically, the prediction model calculation step includes: S201: obtaining a preprocessing sequence by preprocessing according to the time series total page number sequence; S202: Preset a time window, and intercept the preprocessing sequence according to the time window to obtain a prediction sequence; S203: extracting features from the predicted sequence using a principal component analysis algorithm to obtain a feature set; S204: performing feature screening by a filtering method according to the feature set to obtain a prediction feature set, and performing feature conversion by a logarithmic conversion algorithm according to the prediction feature set to obtain a derived feature set; S205: Calculate the predicted quantity through the LSTM model according to the derived feature set.
[0008] Specifically, the specific calculation steps of the filtering method include: S301: Obtaining a feature evaluation result by calculating the feature set through a variance selection method; S302: Sort the feature set according to the feature evaluation result to obtain an ordered feature set; S303: Preset a screening threshold, and screen the ordered feature set according to the screening threshold to obtain a prediction feature set.
[0009] Specifically, the quantity threshold includes a first quantity threshold, a second quantity threshold, and a third quantity threshold. The predicted quantity is divided according to the quantity threshold to obtain the printing device startup quantity, which includes: S401: Determine whether the predicted quantity is greater than the first quantity threshold, if yes, output the first startup quantity; if no, execute step S402; S402: Determine whether the predicted quantity is greater than the second quantity threshold, if yes, output the second start-up quantity; if no, execute step S403; S403: Determine whether the predicted quantity is greater than the third quantity threshold, if yes, output the third startup quantity; if no, output the fourth startup quantity.
[0010] Specifically, the specific calculation steps of the scheduling model include: Calculate the profit margin according to the printing cost, the logistics cost and the transaction price; Obtaining a weight value, and calculating the importance of the file to be printed according to the weight value, the profit margin, the delivery date, and the importance of the customer; The to-be-printed files are sorted according to the file importance to obtain a to-be-printed file sequence set, a number of the to-be-printed file sequence set is obtained, and the execution printing device number is calculated according to the number and the device startup quantity.
[0011] An intelligent printing management system based on document weight, comprising: a data collection module, a prediction module, a calculation module, and a scheduling module; The data collection module is used to obtain order information, order demand information, and the number of pages of the file to be printed, wherein the order demand information includes material specifications, printing quantity, and printing requirements; The prediction module is used to classify the to-be-printed files according to the material specifications and the printing requirements to obtain classified files, count the classified files according to the print quantity and the number of file pages to obtain the total number of classified file pages, add a timestamp according to the total number of classified file pages to obtain the total number of time-series classified file pages, store the total number of time-series classified file pages to obtain a time-series total number of page sequences, and calculate the predicted number according to the time-series total number of page sequences through a prediction model; The calculation module is used to preset a quantity threshold, divide the predicted quantity according to the quantity threshold to obtain a printing device startup quantity, calculate the total weight and total volume of the classified documents according to the material specifications and the total number of pages of the classified documents, obtain a logistics quotation, calculate the logistics cost according to the logistics quotation, the total weight, and the total volume, obtain a material unit cost price, and calculate the printing cost according to the material unit cost price and the total number of pages of the classified documents; The scheduling module is used to calculate the execution printing device number through a scheduling model according to the printing cost, the logistics cost, the order information, and the device startup quantity.
[0012] An electronic device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the program, the intelligent printing management method based on document weight as described above is implemented.
[0013] A storage medium containing computer executable instructions, wherein the computer executable instructions are used to perform the intelligent printing management method based on document weight as described above when executed by a computer processor.
[0014] The beneficial effects of the present invention are: (1) By introducing the long short-term memory (LSTM) neural network model, we can accurately predict the number of pages to be printed in the future, significantly improving the accuracy of printing demand forecasting. The LSTM model can capture the long-term dependencies in time series data and effectively deal with the nonlinear characteristics of the number of pages printed over time, thereby helping enterprises plan printing resources in advance and optimize equipment configuration.
[0015] (2) The system innovatively establishes a quantity threshold mechanism, which automatically calculates and dynamically adjusts the number of printing devices required to run based on the predicted number of pages to be printed in the future, effectively avoiding idle or overloaded devices and achieving reasonable allocation and efficient use of resources. This mechanism not only reduces energy consumption, but also reduces maintenance costs and improves overall operational efficiency.
[0016] (3) In terms of printing task scheduling, the present invention calculates the importance score of each file to be printed by setting weight values based on multiple dimensions such as profit margin, customer importance, and delivery date, and prioritizes them accordingly. This comprehensive consideration ensures that urgent and important printing tasks are given priority, significantly improving customer satisfaction and business response speed.
[0017] (4) Furthermore, the system adopts a modular operation strategy to intelligently distribute the sorted printing tasks to the queues of each running printing device, achieving balanced distribution and efficient scheduling of printing tasks. This innovation solves the problems of task accumulation and processing delays in traditional printing management, ensuring the efficient execution and delivery of printing jobs. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.
[0019] Figure 1 The figure is a flow chart of a smart printing management method based on document weight of the present invention. DETAILED DESCRIPTION
[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.
[0021] See also Figure 1 , an intelligent printing management method based on document weight, Obtaining order information, order requirement information, and the number of pages of a file to be printed, wherein the order requirement information includes material specifications, printing quantity, and printing requirements; Classifying the files to be printed according to the material specifications and the printing requirements to obtain classified files, counting the classified files according to the printing quantity and the number of file pages to obtain the total number of classified files, adding a timestamp according to the total number of classified files to obtain the total number of time-series classified files, storing the total number of time-series classified files to obtain a time-series total number of pages sequence, and calculating the predicted number according to the time-series total number of pages sequence through a prediction model; Preset a quantity threshold, divide the predicted quantity according to the quantity threshold to obtain a printing device startup quantity, calculate the total weight and total volume of the classified documents according to the material specifications and the total number of pages of the classified documents, obtain a logistics quotation, calculate the logistics cost according to the logistics quotation, the total weight, and the total volume, obtain a material unit cost price, and calculate the printing cost according to the material unit cost price and the total number of pages of the classified documents; The number of the printing device is calculated by the scheduling model according to the printing cost, the logistics cost, the order information, and the number of devices started.
[0022] Specifically, the order information includes delivery date, customer importance, and transaction price; the material specifications include material type and material weight, and the material types include but are not limited to A0 paper, A1 paper, A2 paper, A3 paper, and A4 paper; the printing requirements include but are not limited to black and white printing and color printing.
[0023] Specifically, the prediction model calculation step includes: S201: obtaining a preprocessing sequence by preprocessing according to the time series total page number sequence; S202: Preset a time window, and intercept the preprocessing sequence according to the time window to obtain a prediction sequence; S203: extracting features from the predicted sequence using a principal component analysis algorithm to obtain a feature set; S204: performing feature screening by a filtering method according to the feature set to obtain a prediction feature set, and performing feature conversion by a logarithmic conversion algorithm according to the prediction feature set to obtain a derived feature set; S205: Calculate the predicted quantity through the LSTM model according to the derived feature set.
[0024] Specifically, the specific calculation steps of the filtering method include: S301: Obtaining a feature evaluation result by calculating the feature set through a variance selection method; S302: Sort the feature set according to the feature evaluation result to obtain an ordered feature set; S303: Preset a screening threshold, and screen the ordered feature set according to the screening threshold to obtain a prediction feature set.
[0025] Specifically, the quantity threshold includes a first quantity threshold, a second quantity threshold, and a third quantity threshold. The predicted quantity is divided according to the quantity threshold to obtain the printing device startup quantity, which includes: S401: Determine whether the predicted quantity is greater than the first quantity threshold, if yes, output the first startup quantity; if no, execute step S402; S402: Determine whether the predicted quantity is greater than the second quantity threshold, if yes, output the second start-up quantity; if no, execute step S403; S403: Determine whether the predicted quantity is greater than the third quantity threshold, if yes, output the third startup quantity; if no, output the fourth startup quantity.
[0026] It should be noted that the printing device startup quantity includes the first startup quantity, the second startup quantity, the third startup quantity, and the fourth startup quantity.
[0027] Specifically, the specific calculation steps of the scheduling model include: Calculate the profit margin according to the printing cost, the logistics cost and the transaction price; Obtaining a weight value, and calculating the importance of the file to be printed according to the weight value, the profit margin, the delivery date, and the importance of the customer; The calculation formula for the importance of the file to be printed is: , in, Y is the importance of the file to be printed, W 1 、W 2 、W 3 is the weight value, A 1 is the profit margin, A 2 For the delivery date, A 3 Importance for customers; The to-be-printed files are sorted according to the file importance to obtain a to-be-printed file sequence set, a number of the to-be-printed file sequence set is obtained, and the execution printing device number is calculated according to the number and the number of device startups. The execution printing device number calculation formula is: , in, Y To execute the print device number, a For the number, b The number of startups for the device.
[0028] An intelligent printing management system based on document weight, comprising: a data collection module, a prediction module, a calculation module, and a scheduling module; The data collection module is used to obtain order information, order demand information, and the number of pages of the file to be printed, wherein the order demand information includes material specifications, printing quantity, and printing requirements; The prediction module is used to classify the to-be-printed files according to the material specifications and the printing requirements to obtain classified files, count the classified files according to the print quantity and the number of file pages to obtain the total number of classified file pages, add a timestamp according to the total number of classified file pages to obtain the total number of time-series classified file pages, store the total number of time-series classified file pages to obtain a time-series total number of page sequences, and calculate the predicted number according to the time-series total number of page sequences through a prediction model; The calculation module is used to preset a quantity threshold, divide the predicted quantity according to the quantity threshold to obtain a printing device startup quantity, calculate the total weight and total volume of the classified documents according to the material specifications and the total number of pages of the classified documents, obtain a logistics quotation, calculate the logistics cost according to the logistics quotation, the total weight, and the total volume, obtain a material unit cost price, and calculate the printing cost according to the material unit cost price and the total number of pages of the classified documents; The scheduling module is used to calculate the execution printing device number through a scheduling model according to the printing cost, the logistics cost, the order information, and the device startup quantity.
[0029] An electronic device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the program, the intelligent printing management method based on document weight as described above is implemented.
[0030] A storage medium containing computer executable instructions, wherein the computer executable instructions are used to perform the intelligent printing management method based on document weight as described above when executed by a computer processor.
[0031] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.
[0032] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0033] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).
[0034] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.
Claims
1. An intelligent printing management method based on document weight, characterized in that: include: Obtaining order information, order requirement information, and the number of pages of a file to be printed, wherein the order requirement information includes material specifications, printing quantity, and printing requirements; Classifying the files to be printed according to the material specifications and the printing requirements to obtain classified files, counting the classified files according to the printing quantity and the number of file pages to obtain the total number of classified files, adding a timestamp according to the total number of classified files to obtain the total number of time-series classified files, storing the total number of time-series classified files to obtain a time-series total number of pages sequence, and calculating the predicted number according to the time-series total number of pages sequence through a prediction model; Preset a quantity threshold, divide the predicted quantity according to the quantity threshold to obtain a printing device startup quantity, calculate the total weight and total volume of the classified documents according to the material specifications and the total number of pages of the classified documents, obtain a logistics quotation, calculate the logistics cost according to the logistics quotation, the total weight, and the total volume, obtain a material unit cost price, and calculate the printing cost according to the material unit cost price and the total number of pages of the classified documents; The number of the printing device is calculated by the scheduling model according to the printing cost, the logistics cost, the order information, and the number of devices started.
2. The intelligent printing management method based on document weight according to claim 1, characterized in that: The order information includes delivery date, customer importance, and transaction price; the material specifications include material type and material weight, and the material types include but are not limited to A0 paper, A1 paper, A2 paper, A3 paper, and A4 paper; the printing requirements include but are not limited to black and white printing and color printing.
3. The intelligent printing management method based on document weight according to claim 1, characterized in that: The prediction model calculation step includes: S201: obtaining a preprocessing sequence by preprocessing according to the time series total page number sequence; S202: Preset a time window, and intercept the preprocessing sequence according to the time window to obtain a prediction sequence; S203: extracting features from the predicted sequence using a principal component analysis algorithm to obtain a feature set; S204: performing feature screening by a filtering method according to the feature set to obtain a prediction feature set, and performing feature conversion by a logarithmic conversion algorithm according to the prediction feature set to obtain a derived feature set; S205: Calculate the predicted quantity through the LSTM model according to the derived feature set.
4. The intelligent printing management method based on document weight according to claim 2, characterized in that: The specific calculation steps of the filtering method include: S301: Obtaining a feature evaluation result by calculating the feature set through a variance selection method; S302: Sort the feature set according to the feature evaluation result to obtain an ordered feature set; S303: Preset a screening threshold, and screen the ordered feature set according to the screening threshold to obtain a prediction feature set.
5. The intelligent printing management method based on document weight according to claim 1, characterized in that: The quantity thresholds include a first quantity threshold, a second quantity threshold, and a third quantity threshold. The predicted quantity is divided according to the quantity thresholds to obtain the number of printing device startups, including: S401: Determine whether the predicted quantity is greater than the first quantity threshold, if yes, output the first startup quantity; if no, execute step S402; S402: Determine whether the predicted quantity is greater than the second quantity threshold, if yes, output the second start-up quantity; if no, execute step S403; S403: Determine whether the predicted quantity is greater than the third quantity threshold, if yes, output the third startup quantity; if no, output the fourth startup quantity.
6. The intelligent printing management method based on document weight according to claim 1, characterized in that: The specific calculation steps of the scheduling model include: Calculate the profit margin according to the printing cost, the logistics cost and the transaction price; Obtaining a weight value, and calculating the importance of the file to be printed according to the weight value, the profit margin, the delivery date, and the importance of the customer; The to-be-printed files are sorted according to the file importance to obtain a to-be-printed file sequence set, a number of the to-be-printed file sequence set is obtained, and the execution printing device number is calculated according to the number and the device startup quantity.
7. An intelligent printing management system based on document weight, characterized in that: include: Data collection module, prediction module, calculation module, scheduling module; The data collection module is used to obtain order information, order demand information, and the number of pages of the file to be printed, wherein the order demand information includes material specifications, printing quantity, and printing requirements; The prediction module is used to classify the to-be-printed files according to the material specifications and the printing requirements to obtain classified files, count the classified files according to the print quantity and the number of file pages to obtain the total number of classified file pages, add a timestamp according to the total number of classified file pages to obtain the total number of time-series classified file pages, store the total number of time-series classified file pages to obtain a time-series total number of page sequences, and calculate the predicted number according to the time-series total number of page sequences through a prediction model; The calculation module is used to preset a quantity threshold, divide the predicted quantity according to the quantity threshold to obtain a printing device startup quantity, calculate the total weight and total volume of the classified documents according to the material specifications and the total number of pages of the classified documents, obtain a logistics quotation, calculate the logistics cost according to the logistics quotation, the total weight, and the total volume, obtain a material unit cost price, and calculate the printing cost according to the material unit cost price and the total number of pages of the classified documents; The scheduling module is used to calculate the execution printing device number through a scheduling model according to the printing cost, the logistics cost, the order information, and the device startup quantity.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the intelligent printing management method based on document weight as described in any one of claims 1-6 is implemented.
9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to execute the intelligent printing management method based on document weight as described in any one of claims 1-6.