Intelligent printing job scheduling method and device and storage medium

Abstract

The invention relates to the technical field of printing control, and discloses an intelligent printing job scheduling method and device and a storage medium, and the method comprises the steps: obtaining the production process, performance and layout information of each job unit, carrying out the digital simulation based on the information, building a basic and deep digital model, collecting task target data, and simulating a job scheduling scheme. The method comprises the steps of obtaining equipment load information, analyzing logistics evaluation information of different scheduling schemes, evaluating each scheduling plan, selecting an optimal scheme, issuing a job scheduling strategy, tracking the equipment load in real time and adjusting a job scheduling plan, and the method optimizes job scheduling through digital simulation, prediction and evaluation, effectively improves the production efficiency, reduces the fluctuation of the equipment load, and improves the working efficiency. The flexibility and stability of a production line are enhanced, and the problem that in the prior art, the efficiency is poor due to the fact that printing process plans are controlled through artificial experience is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of printing control, and particularly to an intelligent printing job scheduling method and device and a storage medium. BACKGROUND

[0002] The job scheduling of an intelligent printing production line is a complex and variable process, which involves multiple job units, complex production processes, and balancing of equipment load. In traditional printing production lines, job scheduling is usually completed based on manual experience or simple rules, which may result in unreasonable allocation of equipment resources, low production efficiency, and sudden problems in the production process (such as equipment overload or capacity bottlenecks). A printing production line usually involves multiple job units (such as printing machines, post-processing equipment, packaging equipment, etc.), each unit has different production tasks and performance requirements, and these tasks sometimes need to be executed in parallel and sometimes need to be performed in a specific time sequence. Coordination and scheduling are complex, and the equipment load and running state of different job units are crucial to the efficiency and stability of the production line. Excessive equipment load may cause equipment failure, and low load may result in resource waste. In addition to the management of equipment load, logistics transportation is also an important factor affecting production efficiency. Raw materials, semi-finished products, and finished products need to be transferred between different job units through a transportation system in the production process. Reasonable logistics scheduling can significantly improve production efficiency and reduce waiting and idle time. In the production process, task targets, equipment operating conditions, and logistics requirements may change, so dynamic adjustments need to be made to the production plan. Traditional methods often cannot respond to these changes in real time and accurately, resulting in inflexible production scheduling. SUMMARY

[0003] The present application aims to provide an intelligent printing job scheduling method, device and storage medium, which aims to solve the problem of poor efficiency caused by manual experience in controlling printing process planning in the prior art.

[0004] The present application is implemented as follows. In a first aspect, the present application provides an intelligent printing job scheduling method, comprising: Collecting information of each job unit in an intelligent printing production line, and performing digital simulation of intelligent production process conditions and logistics transportation relationships of the intelligent printing production line based on the results of information collection to obtain a basic digital model and a deep digital model; Collecting task target data of the intelligent printing production line, and simulating a unit job scheduling scheme for the task target data based on the basic digital model to obtain a plurality of job scheduling plans and corresponding equipment load information; According to the deep digital model, evaluating the logistics transportation execution of various job scheduling plans to obtain logistics evaluation information of various job scheduling plans; The device load information and logistics evaluation information of various job scheduling plans are evaluated, a job scheduling plan is generated, and each job unit is scheduled.

[0005] In a second aspect, the present application provides an intelligent printing job scheduling device for implementing the intelligent printing job scheduling method of any one of the first aspect, comprising: A model simulation module is configured to collect information of each job unit in the intelligent printing production line, to simulate the intelligent production process and logistics delivery relationship of the intelligent printing production line based on the collected information, to obtain a basic digital model and a deep digital model. A scheme simulation module is configured to collect task target data of the intelligent printing production line, and simulate a job scheduling scheme of each unit based on the task target data and the basic digital model, to obtain a plurality of job scheduling plans and device load information corresponding to each job scheduling plan. A delivery analysis module is configured to evaluate logistics delivery execution of each job scheduling plan based on the deep digital model, to obtain logistics evaluation information of each job scheduling plan. A plan execution module is configured to evaluate each job scheduling plan based on the device load information and the logistics evaluation information, to generate a job scheduling plan, and to schedule each job unit.

[0006] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, causes the processor to execute the intelligent printing job scheduling method of any one of the first aspect.

[0007] The present application provides an intelligent printing job scheduling method, which has the following advantages: The present application obtains production process, performance and layout information of each job unit, simulates the information based on the information, establishes a basic and deep digital model, collects task target data, simulates a job scheduling scheme, obtains device load information, analyzes logistics evaluation information of different scheduling schemes, evaluates each scheduling plan, selects the best scheme and publishes a job scheduling strategy, and tracks device load in real time, adjusts the job scheduling plan. The method optimizes job scheduling through digital simulation, prediction and evaluation, effectively improves production efficiency, reduces device load fluctuation, enhances the flexibility and stability of the production line, and solves the problem of poor efficiency caused by manual experience control of printing process plan in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 is a step schematic diagram of an intelligent printing job scheduling method provided by an embodiment of the present application; Figure 2Fig. 1 is a structural schematic diagram of an intelligent printing job scheduling device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0009] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0010] The implementation of the present application will be described in detail below with reference to specific embodiments.

[0011] Referring to Figure 1 , Figure 2 , a preferred embodiment provided by the present application is shown.

[0012] In a first aspect, the present application provides an intelligent printing job scheduling method, comprising: S1: obtaining production process information, job performance information and position information of each job unit in an intelligent printing production line; S2: digitally simulating intelligent production process conditions and logistics transportation relationships of the intelligent printing production line according to the production process information, job performance information and position information of each job unit, to obtain a basic digital model and a deep digital model; S3: collecting task target data of the intelligent printing production line, and simulating a unit job scheduling scheme according to the task target data and the basic digital model, to obtain a plurality of job scheduling plans and equipment load information corresponding to each job scheduling plan; S4: evaluating logistics transportation execution of each job scheduling plan according to the deep digital model, to obtain logistics evaluation information of each job scheduling plan; S5: evaluating each job scheduling plan according to the equipment load information and the logistics evaluation information, generating a job scheduling plan to schedule each job unit; S6: tracking performance of each job unit executing the job scheduling strategy according to the equipment load information, to obtain a load prediction deviation map, and adjusting the job scheduling plan according to the load prediction deviation map.

[0013] Specifically, in step S1 of the embodiments provided by the present application, the entire intelligent printing production line is modeled by a deep digital model (such as a digital twin model), which contains detailed information of all job units and production links, covers the input and output of each link, production tasks, logistics flow between job units, etc., identifies each job unit on the production line, determines the specific process link it is in, such as raw material storage, printing process, quality inspection, packaging, and delivery, and records the job units and their roles at each link.

[0014] More specifically, real-time data of each job unit is collected by sensors and real-time data acquisition systems (such as industrial Internet of Things devices), including but not limited to job status, process parameters, operating conditions, etc., which provides the basis for analyzing the production process, establishes an integrated process information management system, tracks the real-time production status of each job unit and the information of the link it is in, and automatically identifies any changes or problems in the production process and provides feedback and records, providing accurate tracking and management of each job unit and the production process link it is in, optimizing the overall process arrangement of the production line, ensuring that each job unit can run efficiently in the appropriate link, avoiding bottlenecks or task mismatches, providing data support for subsequent scheduling optimization, and ensuring the flexibility and efficiency of the production line.

[0015] More specifically, important production performance indicators (KPIs) for each job unit are determined, such as production speed, production efficiency, quality pass rate, equipment running time, downtime, etc., and actual running data of each job unit is collected in real time by sensors, device monitoring systems, and production execution systems (MES), including device load, running speed, fault records, work efficiency, etc., and the collected performance data is analyzed to generate performance reports for each job unit. By comparing historical data and preset standards, the work performance of each job unit is evaluated, and based on real-time performance data and early warning systems, performance deviations of job units can be fed back to guide dynamic adjustment of the production line, avoid low production efficiency caused by performance decline, realize real-time performance monitoring of each job unit, ensure improvement of production quality and efficiency, find potential performance problems or equipment failures through data analysis, provide timely warning and intervention, reduce losses in the production process, provide decision support, help production managers optimize production operations and equipment utilization, and improve the overall efficiency of the production line.

[0016] More specifically, by digital modeling, the layout data of the production line operation unit is collected, which includes the physical location of the operation unit, the work flow path, the equipment configuration, the logistics transportation route and other information. Using 3D scanners, laser radars (LIDAR), industrial Internet of Things devices and other means, the spatial layout of the operation unit in the production line and related facilities are accurately measured and recorded. Through simulation software (such as CAD, CAM system, etc.), the operation unit layout is optimized, the influence of different layout configurations on production efficiency and logistics efficiency is simulated, the relative position and operation sequence between operation units are optimized, and the layout setting of the operation unit is adjusted according to the real-time data of the production line to ensure that the material flow, operation unit work load and production efficiency are in the best state. A panoramic view of the spatial distribution and layout of the production line operation unit is provided, which helps managers to timely find unreasonable layout problems. Through layout optimization and simulation, the complexity of logistics transportation is reduced, the efficiency of material flow is improved, unnecessary transportation or waiting time is reduced, flexible configuration and dynamic adjustment of the production line are realized, which can adapt to different production demands and maximize the utilization efficiency of resources and space.

[0017] Specifically, in step S2 of the embodiments provided by the present application, production process information, operation performance information and layout setting information are collected from each operation unit to ensure data completeness and accuracy. These information includes: production process of operation unit (such as printing, quality inspection, packaging and other links), performance parameters of each operation unit (such as capacity, efficiency, downtime, etc.), unit layout (such as equipment location, logistics path, material flow direction, etc.).

[0018] More specifically, using suitable digital modeling tools such as CAD (computer aided design), CAE (computer aided engineering), MES (manufacturing execution system) and the like, the collected data is converted into a basic digital model. According to the collected operation unit information, a basic digital model is established, which includes the structure, function, layout and material flow path of each operation unit in the production line. The model simulates the inflow and outflow of materials and their flow through each link in the production process. By associating the logistics transportation information of the operation unit with the operation scheduling, the flow route of the materials and the arrangement of the operation tasks in the production process are created. This model can simulate the interaction of each operation unit in the production process and lay a foundation for the establishment of subsequent deep models. The basic digital model provides a structured framework for the preliminary simulation of the production line, which can intuitively display the basic situation of each operation unit, production process and material flow, help managers understand the basic operation mode of the entire production process, provide data support for production planning and scheduling optimization, lay a data foundation for the construction of subsequent deep digital models, and ensure the accuracy and reliability of the model.

[0019] More specifically, based on the basic digital model, more detailed production data, equipment real-time data, environmental parameters, logistics dynamic data, etc. are input into the deep digital model, including: real-time production data of each operation unit (such as equipment load, process parameters, real-time state, etc.), real-time operation data of the logistics conveying system (such as conveying belt speed, material distribution, etc.), interaction information between operation units (such as task allocation, task priority, material flow path, etc.), using advanced simulation software (such as Simulink, FlexSim, AnyLogic, etc.) to deeply simulate the basic digital model, the simulation content includes: production process simulation: detailed simulation of interaction between operation units, material flow, task arrangement, etc., to verify the running effect of the production line under different operation scheduling schemes; logistics transportation simulation: based on the layout of operation units, material flow path, transportation mode, etc., to simulate the bottlenecks, delays and efficiency problems in the logistics transportation process; performance optimization analysis: using deep model to analyze and optimize various performances (such as equipment efficiency, production speed, logistics smoothness, etc.) in the production process.

[0020] More specifically, in the deep digital simulation process, the accuracy of the model is verified by comparing the simulation results with the actual production data. According to the feedback results, adjust the model parameters, optimize the operation scheduling and logistics transportation scheme, use 3D graphics, dynamic visual dashboard, etc. to display the simulation results of the deep digital model, help managers to master the running status and potential problems of the production line in real time, the deep digital model can simulate the details of the whole production process, help the production line managers to fully understand the real-time running situation of each operation unit, logistics transportation system and production process, can identify and solve the potential bottlenecks, logistics problems and resource waste in production, provide data-based decision support, provide optimization scheme, help to improve the production efficiency, equipment utilization rate and logistics transportation efficiency of the production line, reduce waiting time, transportation cost and production delay, deep simulation can predict the impact of different operation scheduling, equipment failure, logistics interruption, etc. on the production line, help to conduct risk assessment and emergency plan formulation.

[0021] More specifically, the basic digital model and the deep digital model are integrated to ensure data intercommunication and information sharing between the two, the basic model provides a structural framework for the production process, while the deep model simulates dynamic changes and actual running states in the production process in depth. By fusing different levels of data of the production line (such as physical layout, equipment performance, real-time data, etc.), the two models are integrated into an efficient digital model that can reflect each level of the production line in real time. Based on the integrated model, real-time scheduling optimization and logistics transportation adjustment are carried out, and through dynamic simulation of the model, the coordination of each link of the production line is optimized to ensure efficient execution of production tasks, realizing seamless connection of the basic digital model and the deep digital model, ensuring high matching and synchronization of the production process and logistics transportation, improving the flexibility and response speed of the production line, and being able to real-time adjust production plan and scheduling strategy to quickly respond to changing market demand or production environment. Through the integrated comprehensive model, problems can be predicted and solutions can be provided in real time in actual production, minimizing losses and resource waste in production.

[0022] Specifically, in step S3 of the embodiments provided by the present application, the composition of the task target data is determined, which usually includes the following information: production demand, including production order, product type, quantity, delivery period, etc., resource allocation requirement, including resources required by each operation unit, such as raw materials, equipment, tools, labor, etc., time limit, including time requirements of each production link, such as start and end time of task execution, working hour limit, equipment available time, etc., production line constraint condition, such as equipment capacity, operation unit load, process requirement, logistics transmission capacity, etc.

[0023] More specifically, the production task target data is collected through enterprise resource planning system (ERP), production execution system (MES) and the like, and the order, production plan, equipment status, personnel allocation and other information in the system can be imported into the task target data system in real time through the interface. The collected task target data is cleaned and integrated to ensure the integrity and accuracy of the data, for example, to ensure that the correspondence between the production order and the equipment information is clear, the time limit and resource allocation requirement in the task target are matched, and the complete and accurate task target data is obtained to provide a basis for subsequent job scheduling and equipment load prediction. Through data integration and cleaning, the consistency and operability of the task target data are ensured, and problems such as scheduling failure or equipment load overload caused by data errors are avoided.

[0024] More specifically, based on the job unit information, production process, equipment performance, and other data obtained from the basic digital model, a scheduling model is constructed, which needs to be able to handle various constraints and conditions involved in the task target data, such as production time, equipment availability, process requirements, etc., and select appropriate scheduling algorithms, such as heuristic algorithms, genetic algorithms, simulated annealing algorithms, integer programming, etc., which can help generate optimal or suboptimal scheduling schemes under limited resources and constraints, assign task priorities to each job unit according to production needs and order requirements, and optimize the execution order of tasks, and assign tasks to appropriate job units and equipment according to the status, capacity, and process requirements of the equipment.

[0025] More specifically, the task target data is simulated by the scheduling model to generate different scheduling schemes, and the load, task completion time, equipment utilization, and other indicators of each job unit under each scheme are predicted. After obtaining multiple job scheduling schemes through simulation, the best scheduling scheme is selected through evaluation criteria such as production efficiency, equipment load, task completion time, etc., or multiple alternative schemes are generated for decision-makers to refer to. Through the simulation of the scheduling model, multiple job scheduling plans are generated, which can fully consider all limiting factors in the task target data and obtain scheduling schemes that meet production needs. The simulation process can foresee potential problems under different scheduling schemes, such as high equipment load, production bottlenecks, task delays, etc., so that effective prevention and adjustment can be made, and flexible scheduling schemes can be provided for dynamic adjustment according to actual production conditions, improving the response capability of the production line.

[0026] More specifically, based on the task target data and job scheduling scheme, a device load prediction model is established, which needs to consider the load capacity, running time, process requirements, and other factors of each job unit and device. For each job unit, the load under different scheduling schemes is calculated, which usually refers to the workload, utilization rate, etc. of the equipment within a certain time period, and the time series data of equipment load under different scheduling schemes is generated to predict the load of the equipment within different time periods. The equipment load data is generated through graphical tools such as Matplotlib, Tableau, etc. to generate equipment load information, which can show the load changes of each device within different time periods and the differences in equipment load under different job scheduling schemes.

[0027] More specifically, the load prediction atlas of multiple devices is generated at the same time, facilitating comparison of the influence of different job scheduling schemes on the load of each device, identifying devices with excessively high load by analyzing the device load atlas, and then finding scheduling optimization opportunities, such as device load balancing, device expansion or adjusting production rhythm, etc. The device load information provides a clear view of the device usage, helping production line managers to identify which devices are in an overload state or idle state, facilitating the adjustment of production tasks. Through the load atlas, it is possible to predict in advance that the device load is excessively high, there is a bottleneck problem or resource waste, help to optimize production scheduling, and ensure that the device is reasonably utilized. The device load atlas supports comparison of multiple scheduling schemes, helping decision makers to choose the optimal scheme, and improving device utilization and production efficiency.

[0028] More specifically, the device load information under different scheduling schemes is compared, the performance of each scheme in terms of device load, task completion time, production efficiency, etc. is analyzed, and a multi-index decision-making method (such as weighted scoring method, AHP analysis, etc.) is used to comprehensively evaluate different scheduling schemes in combination with production target data, and the most suitable scheduling plan is selected. According to the comparison results of multiple scheduling schemes, the scheduling scheme or resource allocation is adjusted in a timely manner to cope with changes in the actual production process. Through comprehensive comparison of multiple scheduling schemes, it is ensured that the production scheduling scheme not only meets the task target, but also maximizes the utilization efficiency of device resources, providing clear data support and decision basis for production management personnel, helping to optimize job scheduling, and reducing device idle time and overload problems.

[0029] Specifically, in step S4 of the embodiments provided in the present application, the key indicators of logistics transportation execution are determined: logistics evaluation information is a comprehensive evaluation index of logistics process efficiency and quality, and appropriate reference indices need to be selected according to the characteristics of the production line. These reference indices include: transportation time (T): the time required to complete the logistics transportation of each job unit, including the transportation time from one production link to another, logistics cost (C): the cost required for logistics activities during job scheduling, including labor, equipment, transportation tools and other expenses, logistics efficiency (E): measures the efficiency of resource utilization in the logistics transportation process, which is usually measured by the amount of tasks completed per unit of time, material supply reliability (R): the accuracy of material arriving on time in the production line, reflecting the reliability of the logistics system, device load balancing (L): the load state of logistics devices (such as conveyors, forklifts, etc.), evaluating whether they are reasonably utilized.

[0030] More specifically, by data analysis and modeling, the numerical value of each reference index is quantified to ensure that the indicators can be applied in the job scheduling plan. The calculation can be based on historical production data or simulation data, for example: the transportation time (T) can be estimated by the transportation capacity of the equipment and the transportation distance, the logistics cost (C) can be calculated by the cost model of different logistics links (such as unit transportation cost, labor cost, etc.), and the logistics efficiency (E) can be quantified by the transportation time and material throughput. By determining and quantifying each logistics reference index, the performance of the logistics link in the job scheduling plan can be comprehensively evaluated. The reference index provides data support for subsequent comparative analysis, ensuring the comparability of different scheduling plans at the logistics execution level.

[0031] More specifically, according to the deep digital model, multiple job scheduling plans are generated, each of which schedules tasks in different ways, allocates resources, and arranges production processes. For each job scheduling plan, the logistics performance is analyzed through the reference index model of logistics execution. For each plan, the following dimensions can be evaluated: calculate the average time and cost of logistics transportation under different job scheduling plans, evaluate its efficiency, check whether the load of each logistics equipment is balanced, evaluate whether the utilization rate of logistics equipment is optimized, based on the production rhythm and material supply situation of the scheduling plan, analyze whether each job unit can obtain the required materials on time to ensure production continuity.

[0032] More specifically, using the simulation and prediction capabilities of the deep digital model, each job scheduling plan is simulated and analyzed to evaluate the performance of logistics transportation execution. Through machine learning algorithms such as deep neural networks and reinforcement learning, the execution effects of different job scheduling plans are compared, and potential logistics bottlenecks are predicted. Based on the analysis results, strategy optimization and adjustment are carried out, for example, if the logistics cost is high under a certain plan, the material distribution plan can be adjusted or the transportation route can be optimized to reduce the cost; if the logistics efficiency of a certain plan is low, the production rhythm can be adjusted or the equipment load can be optimized to improve the efficiency. The deep digital model can drive analysis through real-time data to comprehensively evaluate the logistics execution of each job scheduling plan, thereby optimizing the logistics link. Based on the deep learning and simulation capabilities, potential problems in different scheduling plans can be automatically identified, such as transportation bottlenecks and equipment overload, and targeted solutions can be provided.

[0033] More specifically, according to the above-mentioned multiple reference indicators, a logistics evaluation information (LEI, Logistics Execution Index) is synthesized, which can be calculated according to the weighted average of different indicators, or can be comprehensively evaluated using a more complex multi-objective decision model. Common methods include: weighted average method: the weights of each reference indicator are set (such as transportation time, cost, etc. each accounts for a certain proportion), and then the overall logistics execution index is calculated by weighted average; fuzzy comprehensive evaluation method: if the relationship between each indicator is complex, fuzzy mathematics method can be used to evaluate the logistics execution index.

[0034] More specifically, for each job scheduling plan, the logistics execution index is calculated to quantify the performance of logistics delivery execution. According to the calculated logistics evaluation information, different job scheduling plans are compared and analyzed, and the plan with the optimal logistics execution index is selected as the optimal solution. The logistics evaluation information provides a comprehensive performance evaluation standard for each job scheduling plan, which is convenient for decision makers to compare and select different plans. Through this indexation method, the logistics performance of different scheduling plans can be quantified, and the basis for subsequent decision-making can be provided.

[0035] More specifically, based on the logistics evaluation information, multiple job scheduling plans are compared and analyzed, and the best performing plan is selected as the execution plan. According to the real-time data feedback during the implementation process, the job scheduling plan is dynamically adjusted to further optimize the logistics execution process, ensuring the best balance of logistics efficiency, cost and equipment load. Through comprehensive analysis of logistics evaluation information, the scheduling plan that best meets the production target can be selected, improving overall logistics efficiency and providing flexible adjustment and optimization space for the production line, ensuring continuous optimization of logistics execution during the production process.

[0036] Specifically, in step S5 of the embodiments provided by the present application, historical operation data of the production line equipment is collected, including the load condition, operation efficiency, maintenance period, failure rate, etc. of each equipment. These data can come from sensors of production equipment, intelligent control systems or real-time monitoring systems such as PLC (Programmable Logic Controller), etc. Based on the historical data, machine learning methods (such as regression analysis, deep learning, time series analysis, etc.) are used to predict equipment load. The model will consider factors such as the running state of the equipment, the number and type of production tasks, the cooperation relationship between equipment, etc. Regression analysis is suitable for simple load prediction. Through equipment running time, production load, task type, etc. Input variables, load prediction is carried out. If the production line is complex and the data volume is large, methods such as deep neural network can be used for load prediction to capture the complex relationship between equipment load and production tasks. The model outputs the equipment load prediction result, draws the equipment load information, and shows the load changes of each equipment in different time periods. This can directly reflect the load condition of the equipment in the production process, help to find the phenomenon of uneven equipment load, overload or idling. Real-time load prediction can provide real-time load data for job scheduling, predict the load condition of the equipment in the future time period, and find potential load bottlenecks or resource waste in advance. Through reasonable prediction of equipment load, equipment utilization can be optimized, equipment overload or idling can be avoided, and the overall efficiency of the production line can be improved.

[0037] More specifically, according to the foregoing steps, considering multiple reference indicators (such as transportation time, logistics cost, material supply reliability, etc.) of logistics execution, calculating the logistics evaluation information (LEI) of each job scheduling plan, using the weighted score of different indicators to comprehensively evaluate the job scheduling plan, quantitatively comparing different job scheduling plans based on logistics evaluation information, and selecting the job scheduling plan with the best evaluation result as the execution scheme. By comprehensively considering multiple key indicators of logistics execution, the logistics execution capability of different scheduling plans can be comprehensively evaluated, and data support is provided for job scheduling decision-making. According to the evaluation result, the best scheduling plan is selected, so as to maximize logistics efficiency, reduce transportation cost, and improve overall production efficiency.

[0038] More specifically, in combination with equipment load information and logistics evaluation information, a comprehensive evaluation is conducted, and the equipment load and logistics execution of each job scheduling plan are considered to ensure the selection of the optimal solution. During the evaluation process, the system can automatically adjust the scheduling parameters through optimization algorithms (such as genetic algorithm, particle swarm optimization, etc.), optimize equipment load distribution, reduce logistics transportation time and cost, and improve overall efficiency. Based on the evaluation results, the job scheduling plan with the best comprehensive performance is selected, which not only meets the production needs but also ensures balanced equipment load and maximized logistics efficiency. By dynamically adjusting the job scheduling plan, the production line scheduling strategy can be automatically optimized under real-time conditions, maximizing resource utilization. Using a data-driven evaluation method, the subjective bias caused by traditional manual decision-making is avoided, ensuring that scheduling decisions are more scientific and accurate.

[0039] More specifically, according to the selected optimal job scheduling plan, a detailed job scheduling plan is generated. The scheduling plan will be specific to each production link, including production task allocation, equipment scheduling, logistics path arrangement, etc. The scheduling plan is issued to the execution system of the intelligent printing production line, and through the integrated intelligent control system, scheduling commands are automatically sent to the equipment and logistics system, and the scheduling execution situation is monitored in real time. The scheduling system will dynamically adjust the scheduling plan according to the real-time feedback of the production line (such as equipment status, production progress, etc.), ensuring smooth production process. By automatically issuing job scheduling plans through intelligent systems, manual intervention is reduced, and the accuracy and efficiency of production scheduling are improved. The scheduling system can monitor the execution process in real time to ensure that the plan can be accurately implemented and quickly adjusted according to actual production conditions to avoid production interruptions or delays.

[0040] More specifically, after the job scheduling strategy is issued, the whole process is monitored, including equipment load, logistics transportation progress, production task completion, etc. According to the actual execution of the production line, feedback data enters the system for adjustment and optimization. For example, if a device fails, the scheduling system will redistribute tasks to avoid production stagnation. Finally, a job scheduling execution report is generated for effect analysis, evaluation of scheduling plan execution efficiency, and provision of basis for subsequent scheduling optimization. Through intelligent monitoring systems, scheduling strategies can be efficiently executed, and problems can be quickly adjusted. Through real-time feedback of execution data, future scheduling strategies are continuously optimized to improve overall production efficiency and resource utilization.

[0041] Specifically, in step S6 of the embodiment provided by the present application, first, the installation monitoring system (such as sensors, PLC systems, etc.) collects real-time data of each work unit, the monitoring indicators include equipment running time, load state, temperature, speed, energy consumption, fault state, etc., according to the previously generated equipment load information, the predicted load of each work unit is calculated in real time, and compared with the actual load, the load prediction map provides the expected load of each work unit in different time periods, and the actual load data is the real-time data obtained from the production line sensors.

[0042] More specifically, by calculating the load prediction deviation of each work unit, a deviation value is obtained, which represents the difference between the actual load and the predicted load. If the deviation is too large, it means that the prediction is problematic or there is an abnormality in the work unit. By obtaining accurate equipment performance data through real-time monitoring system, the running state of each work unit can be understood in time, and the load prediction deviation map can intuitively reflect the difference between prediction and actual, providing key basis for subsequent scheduling adjustment.

[0043] More specifically, by analyzing the load prediction deviation map in depth, the work units with large deviations are identified. These deviations may be caused by equipment failure, abnormal load fluctuation, improper scheduling, etc. Using data analysis algorithms (such as anomaly detection, clustering analysis, etc.) to identify areas with large prediction errors and classify them, the abnormality of deviation can be divided into: continuous deviation, indicating that the system has not met the load demand for a long time, which may be caused by equipment aging, improper task allocation, etc., short-term fluctuation deviation, referring to the load fluctuation in a short period of time, which may be caused by equipment failure, unstable input materials, etc.

[0044] More specifically, through the analysis of the deviation map, the system will generate load adjustment signals according to the deviation value. These signals will indicate how the scheduling system should adjust the equipment load or work unit to reduce the deviation and optimize the production line operation. Through the analysis of the load prediction deviation map, the work units with large deviations and potential problems can be quickly identified. Through in-depth analysis of the deviation, accurate adjustment suggestions can be provided for the scheduling system, so as to optimize the production line scheduling.

[0045] More specifically, based on the analysis results of the load prediction deviation map, the scheduling system can automatically adjust the job scheduling plan, for example, if the load deviation of a certain job unit is large, the scheduling system can adjust by transferring the tasks originally assigned to the high-load job unit to the lower-load job unit, appropriately extending or shortening the running time of the equipment, balancing the load of each job unit, optimizing the material transfer path, job order, etc. according to the real-time state of the production line, to reduce load fluctuations and improve production efficiency, if the load deviation is caused by equipment failure or performance decline, the system can schedule maintenance resources, schedule backup equipment, or take other remedial measures, by dynamically adjusting the job scheduling plan, the load deviation in production can be responded to in real time, ensuring the stable operation of the production line, by optimizing resource allocation and task scheduling, the equipment utilization rate can be improved, and the idle time and load overload situation can be reduced.

[0046] More specifically, after adjusting the job scheduling plan, the load status of each job unit is continuously monitored, and it is checked whether the load prediction deviation has been reduced or eliminated, if the adjusted scheduling strategy still has a large deviation, the system will further optimize the adjustment strategy according to the new load data, and iterates continuously until the load prediction deviation reaches the acceptable range, through continuous feedback and optimization, the job scheduling system can continuously improve the scheduling effect, ensure the stability of the production line in long-term operation, and verify that the adjusted effect can ensure that the scheduling system can efficiently execute the adjusted plan in actual operation, and improve the overall efficiency of production.

[0047] More specifically, the system generates a detailed scheduling feedback report according to the scheduling adjustment results, the report contains deviation analysis, adjustment scheme, effect evaluation, etc., according to the feedback report, the management personnel can make further decisions on the production line scheduling. The report can provide data support for future production plan optimization, further improve the decision-making ability of the intelligent scheduling system, through the generation of feedback reports, data support can be provided for the management layer to help them make more scientific and accurate decisions, the whole adjustment and feedback process realizes the intelligent management of the production line, effectively improves the production efficiency and reduces the risk of equipment overload.

[0048] The present application provides an intelligent printing job scheduling method, which has the following beneficial effects: The application obtains production process, performance and layout information of each work unit, carries out digital simulation based on the information, establishes basic and deep digital models, collects task target data, simulates work scheduling scheme, obtains equipment load information, analyzes logistics evaluation information of different scheduling schemes, evaluates each scheduling plan, selects the best scheme and issues work scheduling strategy, tracks equipment load in real time, and adjusts work scheduling plan. The method optimizes work scheduling through digital simulation, prediction and evaluation, effectively improves production efficiency, reduces equipment load fluctuation, enhances flexibility and stability of the production line, and solves the problem of poor efficiency caused by manual experience control of printing process plan in the prior art.

[0049] Preferably, the step of obtaining production process information, work performance information and position information of each work unit in the intelligent printing production line comprises: S11: obtaining the unit work application field of each work unit in the intelligent printing production line, and analyzing the production process link order between each work unit according to the unit work application field of each work unit, to obtain the production process information of each work unit; wherein the production process information is used to describe the production process link to which the work unit belongs in the intelligent printing production line; S12: collecting and extracting features of unit work performance data of each work unit according to the unit work application field of each work unit, to obtain the work performance information of each work unit; wherein the production work performance information is used to describe the work performance that the work unit can play in its own unit work application field in the production process environment; S13: conducting on-site data survey on the site where the intelligent printing production line is arranged, to obtain work scene layout data, and collecting specific positioning data of each work unit according to the work scene layout data, to obtain the position information of each work unit.

[0050] Specifically, the types and functions of each work unit in the intelligent printing production line are determined, which can be a printing unit, a coating unit, a drying unit, a post-processing unit, etc. Each work unit has its specific application field, such as printing equipment responsible for printing text and graphics, coating unit responsible for coating ink or protective layer, etc. Each work unit has specific tasks and application fields, such as some work units may only be used in specific processes, and other work units may be multipurpose. The application field of each work unit is defined according to these tasks, and the applicable production steps and process requirements are recorded. By defining the application field of each work unit, the functions and roles of each unit in the production process can be clearly understood, resource waste and overlapping of work units can be avoided, and the application field of each work unit provides an important basis for subsequent production process link order analysis.

[0051] More specifically, according to the application field of each job unit, analyze their arrangement order in the entire production process, for example, printing job units are usually at the front end of the production process, and post-processing job units are at the end of the production line, the system derives the production process order between each job unit according to the functional connection between different units, through the analysis of the relationship between job units, a complete production process model is established, and the link information of each job unit in the production process is obtained, for example, a job unit may be used as an initial step or a component of an intermediate link, and another may be used as a key of a later processing link. Through the analysis of the application field of each job unit and the production process link, the position and role of the job unit in the production line can be determined, ensuring smooth process, avoiding bottlenecks and resource waste, providing accurate data support for subsequent production scheduling and process optimization, and helping to optimize the production efficiency of each link.

[0052] More specifically, through sensors, monitoring systems and other devices, real-time performance data of each job unit is collected, such as running speed, energy efficiency, load, failure rate, temperature, etc. These data can reflect the running status of the job unit, and the collected performance data is analyzed and features are extracted. Common features include job unit efficiency, output per unit time, device running stability, etc. Using machine learning algorithms, key features reflecting job unit performance can be extracted from historical data, such as energy efficiency and failure warning indicators. Through performance data collection and feature extraction, job performance information of each job unit is obtained, which can be used to describe the working capacity and efficiency of the job unit in a specific production environment. Through performance data collection and feature extraction of job units, the working status of each job unit can be tracked and evaluated in real time, and potential performance problems can be found in time. Through the analysis of performance data of job units, decision support can be provided for the optimization of production line, and the overall production efficiency can be improved.

[0053] More specifically, visit the site where the intelligent printing production line is located, collect relevant data of the production line layout, and survey the content including the total area of the production line, the placement position of the equipment, the distance between the job units, etc. According to the data surveyed on site, record the layout of the production line and the specific position of the job units. These data are of great significance for optimizing the layout of the production line and improving the collaboration efficiency of job units. Through on-site survey and collection of layout data, the spatial layout of the production line can be reasonably planned, excessive crowding or idleness can be avoided, and production efficiency can be improved. The survey data provide on-site basis for subsequent adjustment and layout optimization of job units, which helps to reduce interference between devices and improve production efficiency.

[0054] More specifically, the precise position of each work unit is obtained through positioning technology such as GPS, RFID, laser ranging, etc., and these positioning data include the geographic coordinates, spatial height, relative position to other work units, etc. of the work unit. Combined with the actual layout and positioning data, the relative position of the work unit is further optimized to ensure reasonable distance and coordination between work units, improve the collaboration effect between work units, and through accurate positioning data collection, the layout of each work unit can be ensured to meet the working requirements of the production line, avoiding equipment conflicts and improving the overall efficiency of the production line. According to the positioning data of the work unit, real-time adjustment can be made during the production process to ensure the flexibility and responsiveness of the production line in actual operation.

[0055] Preferably, the step of simulating the intelligent production process and logistics transportation relationship of the intelligent printing production line according to the production process information, work performance information and position information of each work unit to obtain the basic digital model and the deep digital model comprises: S21: digitally simulating the unit performance of each work unit according to the work performance information of each work unit to obtain a work simulation model corresponding to each work unit; S22: model correlation and connection of the work simulation model of each work unit based on the intelligent production process of the intelligent printing production line according to the production process information of each work unit to obtain a basic digital model for digitally feeding back the intelligent production process of the intelligent printing production line; S23: model correlation and connection of each work simulation model in the basic digital model based on the logistics transportation relationship of each work unit in the intelligent printing production line according to the position information of each work unit to obtain a deep digital model for digitally feeding back the logistics transportation relationship between each work unit in the intelligent printing production line.

[0056] Specifically, based on the work performance information of each work unit, data related to unit performance such as running speed, load, output, failure rate, etc. are collected. Through monitoring equipment and sensors, data are obtained and preprocessed, and key features related to performance are extracted. Mathematical modeling methods are used to digitally simulate the performance of each work unit, convert data into corresponding work unit models, and simulate their performance under different working conditions. These simulation models can predict the production capacity, efficiency, failure probability, etc. of the work unit.

[0057] More specifically, each job unit has a corresponding digital model that describes its performance and working state. These models can make predictions based on input working conditions such as load, speed, temperature, etc. to help understand the performance of the job unit under specific conditions. By performing digital simulation of each job unit's performance, real-time performance evaluation and prediction can be performed during production, improving production efficiency. Based on the simulation results of the job units, potential bottlenecks can be identified, and equipment failures can be warned in advance, thereby supporting dynamic optimization of the production line.

[0058] More specifically, based on the production process information of each job unit, a mathematical model of the production process is established, which describes the interdependence between job units, the working order, and the resource flow. The job simulation model of each job unit is associated with the production process link model, and the working state, working order, and other information of each job unit are connected into a complete production line model through mathematical operations or simulation software. Based on the above model association, a basic digital model for intelligent production process condition digital feedback is established. This model can reflect the overall status of the production line in real time during production, helping to monitor the operation effect and efficiency of the production line. The basic digital model enables the production process status to be fed back in real time, helping operators understand the overall performance of the production line, such as production progress, process quality, etc. By associating job simulation models with production process links, the scheduling efficiency of the production line can be improved, idle time or excessive load can be reduced, and the overall production efficiency of the production line can be improved.

[0059] More specifically, based on the layout setting information of the job units, the physical positions, distances, and logistics transportation paths between each job unit are defined, which includes material flow from one job unit to another, connection between devices, working path of transmission belts, transport vehicles, etc. Using the layout setting information of the job units, a logistics transportation relationship model between each job unit is established, which considers factors such as material transportation time, transportation method, and equipment efficiency. The logistics transportation relationship model is combined with the basic digital model to form a deep digital model. This model can simulate material flow, equipment collaboration, and output process in the production line, reflect logistics transportation conditions between job units, and provide more in-depth production line status feedback. The deep digital model can accurately simulate material flow and equipment collaboration between job units, help identify logistics bottlenecks, reduce transportation time, and optimize the use efficiency of transportation equipment. Through the deep digital model, different material flow conditions such as queuing, distribution, and transportation methods between different job units can be simulated, thereby optimizing logistics strategies and reducing the total cost of the production line.

[0060] More specifically, by collecting data of the production line in real time, verifying the accuracy of the basic digital model and the deep digital model, comparing the production data (such as production speed, equipment failure, transportation time, etc.) with the model output, judging whether the prediction result of the model is consistent with the actual production state, adjusting the parameters of the digital model based on real-time feedback, optimizing the performance simulation of the job unit, the correlation of the process link and the logistics transportation relationship, ensuring that the model can adapt to the changes of the actual production environment, through real-time feedback and adjustment, the production process can be continuously optimized, and the response speed and adaptability of the production line can be improved. The continuous optimization and adjustment of the model enable the production line to cope with different production demands and unexpected situations, and improve the flexibility and stability of production.

[0061] Preferably, the step of collecting task target data of the intelligent printing production line and simulating unit job scheduling schemes according to the task target data based on the basic digital model to obtain a plurality of job scheduling plans and device load information corresponding to each job scheduling plan comprises: S31: Collecting information of each production task to be performed by the intelligent printing production line and priority weights corresponding to each production task information, and statistically analyzing each production task information and corresponding priority weights to obtain task target data of the intelligent printing production line; S32: Distributing tasks of each link of the production process according to the task target data based on the basic digital model to obtain task distribution data of each link of the production process; S33: Analyzing the data correlation of the task distribution data of each link of the production process to obtain job stress information of the intelligent printing production line at each link of the production process; S34: Distributing task target data and deploying performance parameters of the production process connection efficiency best form to the basic digital model according to the job stress information of each link of the production process to obtain theoretical unit configuration characteristics of each unit simulation model in the basic digital model corresponding to the task target data; wherein the theoretical unit configuration characteristics are used to describe the executed job task status and deployed unit performance parameters of the job unit in the production process connection efficiency best form; S35: Distributing each production task information according to the priority weight corresponding to each production task information in the task target data to adapt to the priority weight and the theoretical unit configuration characteristics to obtain a plurality of job scheduling plans corresponding to the theoretical unit configuration characteristics; wherein the job scheduling plan is used to describe the specific allocation mode of each production task information in the task target data to the job unit of each link of the production process, and the specific allocation mode includes the job order of the production task information in the job unit of each link of the production process relative to other production task information; S36: Collect and analyze historical data of the equipment load status of each job unit to obtain the equipment load characteristics of each job unit, and simulate and deduce the equipment load status of the equipment load characteristics of each job unit according to each job scheduling plan to obtain the equipment load information of each job scheduling plan; wherein the equipment load information is used to describe the expected equipment load status of each job unit at each time node when executing the job scheduling plan; S37: Based on the equipment load information of each job scheduling plan, the theoretical unit configuration characteristics of each job unit are constrained by the equipment safety condition to obtain the constrained unit configuration characteristics of each job unit under the premise of safe equipment operation; wherein the constrained unit configuration characteristics are used to describe the executed job task status and deployed unit performance parameters of the job unit when the equipment is safely operated; S38: According to the priority weight corresponding to each production task information in the task target data, the task is specifically allocated to the priority weight and the constrained unit configuration characteristics to obtain several job scheduling plans corresponding to the constrained unit configuration characteristics, and the equipment load characteristics of each job unit are simulated and deduced according to each job scheduling plan to obtain the equipment load information of each job scheduling plan.

[0062] Specifically, the information of each production task that the intelligent printing production line needs to execute is collected, including the detailed description of each task, the execution time, the resource demand, etc. According to the importance, urgency and influence on other tasks of the production task, each task is assigned a priority weight, which is usually determined by production plan, customer demand, equipment state, etc.

[0063] More specifically, the production task information and its corresponding priority weight are statistically analyzed to generate task target data, which not only contains the basic information of the task, but also includes their priority and expected production time, etc. The introduction of priority weight helps to clarify the priority of each production task, thereby providing a basis for subsequent scheduling decision. Through the statistical analysis and arrangement of the task information, the production task can be more accurately managed, laying a foundation for scheduling optimization.

[0064] More specifically, according to the basic digital model, the task demand data of each production task corresponding to the work unit is parsed, for example, a task may need multiple work units to cooperate to complete, and in this step, each task demand is refined into the specific workload and task demand of each work unit. By disassembling the work demand of each task, task demand data is generated to represent the specific demand of each work unit when executing a production task, including working time, resource consumption, etc. The disassembly and parsing of task demand make the specific workload of each work unit clear, providing accurate data for subsequent scheduling decisions and resource allocation.

[0065] More specifically, the task demand data of each link of the production process is analyzed for correlation, and the mutual dependency relationship and possible bottleneck area between work units are found. Through the analysis of the task demand data of the work units, a work pressure distribution diagram of each link of the production process is obtained, which shows which links have high task demand and which links may cause overload or idling. The work pressure distribution analysis can reveal the potential bottlenecks in production, help optimize task scheduling, and improve the balance of the production line. Through the identification of pressure distribution, task allocation can be adjusted more reasonably to avoid excessive load and resource waste.

[0066] More specifically, according to the work pressure distribution and task demand data, the configuration parameters of each work unit in the basic digital model are adjusted, and the configuration of the work units is optimized to achieve the best form of production process connection efficiency. According to the above adjustment, the task execution and performance parameters of each work unit under ideal production conditions are determined, including work order, production task allocation, and unit performance, etc. The theoretical unit configuration characteristics can reflect the optimal task execution condition of the work unit under ideal production state, thereby providing an efficient configuration model for subsequent scheduling.

[0067] More specifically, according to the priority weight of the task and the theoretical unit configuration characteristics, the specific allocation of the task is performed. This step ensures that high-priority tasks can be processed in a timely manner, and the task arrangement of each work unit meets its workload. According to the task allocation, a number of work scheduling plans are generated, each describing the allocation of production tasks between different work units, including work order, resource allocation, etc. Generating different work scheduling plans provides multiple optimization paths for selection, which helps flexible adjustment of production plan. By combining priority weight and unit configuration characteristics, the reasonable scheduling of production tasks can be ensured, which meets the priority of production demand.

[0068] More specifically, historical equipment load data of the job unit is collected and analyzed to identify the regularity and characteristics of equipment load, such as load peak period, equipment operation stability, etc. According to each job scheduling plan, the equipment load characteristics corresponding to each plan are simulated and deduced to obtain equipment load information under different job scheduling plans. Simulation and deduction can foresee the equipment load status under different job scheduling plans, help managers identify possible equipment overload or idle problems in advance, and optimize the use of equipment through load prediction to avoid overload and improve equipment utilization efficiency.

[0069] More specifically, according to the safety operation requirements of the equipment, safety condition constraints are imposed on the equipment load state of each job unit to ensure that the equipment load does not exceed its safe carrying capacity. Under the safety condition of the equipment, the configuration of each job unit is adjusted to obtain the constrained unit configuration characteristics. This characteristic describes the tasks that the equipment can perform and the performance parameters that the equipment can deploy under the safety load limit. Through the adjustment of the constrained unit configuration characteristics, it is ensured that the equipment operates within the safety load range, reducing the risk of equipment failure. Equipment safety constraints help to ensure that the production line operates efficiently within the safety range and avoid overloading or excessive use of equipment.

[0070] More specifically, according to the priority weight and the constrained unit configuration characteristics, the specific allocation of tasks is performed again, and a job scheduling plan that adapts to the constraint condition is generated. For each final job scheduling plan, the equipment load status is simulated and deduced to obtain the final equipment load information. By considering the task priority weight, equipment safety, and equipment load characteristics, the final job scheduling plan is generated to provide accurate guidance for production scheduling. The final load prediction map helps to adjust the job scheduling in real time to ensure smooth operation of the equipment and efficient and safe production process.

[0071] Preferably, the step of evaluating the logistics transportation execution of various job scheduling plans according to the deep digital model to obtain logistics evaluation information of various job scheduling plans comprises: S41: Simulate the logistics transportation status of the job scheduling plan according to the deep digital model to obtain the logistics characteristics of each job unit between each link of the production process of the intelligent printing production line when executing the job scheduling plan; wherein the logistics characteristics are used to describe the transportation mode and transportation time of the work object completed by the job unit of the previous production process link to the job unit of the next production process link; S42: Based on the logistics characteristics of each job unit between each link of the production process of the intelligent printing production line when executing the job scheduling, simulate the logistics transfer accumulation status of the job unit of each link of the production process when executing the job scheduling plan to obtain the logistics transfer accumulation reference characteristics of the job unit of each link of the production process. S43: based on the logistics features of each job unit between each link of the production process when the intelligent printing production line executes the job scheduling, simulating the logistics transfer interaction influence condition of the job units of each link of the production process to execute the job scheduling plan, to obtain the logistics interaction influence reference features of the job units of each link of the production process; S44: comprehensively evaluating the executability of the logistics transfer accumulation reference features and the logistics interaction influence reference features to obtain the logistics evaluation information of the job scheduling plan.

[0072] Specifically, according to the deep digital model, the logistics transfer condition of the job units between each link of the production process under each job scheduling plan is simulated, which involves the simulation of the transfer of the working objects of each job unit in the production process, including the transfer mode and transfer time of materials, products or components, and the distance between different job units, the transfer path, the transfer mode (such as conveyor belt, manual handling, etc.) and time delay are considered in the simulation process, through simulation, the logistics transfer features of each production process link are obtained, which include the mode, time and possible bottlenecks of material or component transfer to the next link job unit after the previous link job unit completes the job, through the simulation of the logistics transfer condition, the logistics flow between each job unit can be understood in detail, which provides basis for subsequent job scheduling optimization, simulation can help to find the potential bottlenecks or bottleneck areas in the logistics process, and timely adjust the job scheduling scheme.

[0073] More specifically, based on the logistics transfer features of the job scheduling plan, the possible accumulation conditions of each job unit in the logistics transfer process in the production process are simulated, the accumulation refers to the accumulation phenomenon caused by low transfer efficiency or delayed material processing during the transfer of materials or products between different production links, the job speed, material backlog and time of each job unit are considered in the simulation, through simulation, the logistics transfer accumulation reference features of each job unit are obtained, which represent the possible accumulation amount and its influence during the transfer of materials or products between job units under a specific job scheduling plan, through the simulation of the accumulation condition, it can be accurately identified which job link may cause logistics congestion or delay, so that corresponding optimization measures are taken, the transfer accumulation features help the production line managers to understand the bottlenecks of material transfer in the production line, and provide guidance for subsequent job scheduling adjustment.

[0074] More specifically, when executing the job scheduling plan, the logistics transportation links of each job unit are often not independent, and there may be interactive influences between them, for example, the transportation delay of a job unit may affect the production efficiency of the subsequent link. By simulating and analyzing the interactive influences between the links of the production process, the influences can be quantified. During simulation, the interdependence between job units, job time, and resource sharing conditions, etc. need to be considered. Through interactive influence simulation, the logistics interactive influence reference features of each job unit are obtained, which describe the interaction between different job units and their influence on the overall logistics transportation. Through simulation of the interactive influence conditions, the mutual influences between different job units can be identified, and the links that may cause production bottlenecks or delays can be found in time. The analysis of logistics transportation interactive influences helps to realize the coordinated operation of the links of the production process and ensures the optimal allocation of resources and tasks.

[0075] More specifically, by comprehensively analyzing the logistics accumulation reference features and the logistics interactive influence reference features, the overall execution effect of each job scheduling plan is evaluated. This step aims to judge the logistics bottlenecks and risks that may occur during the actual execution of each job scheduling plan through the combination of the two, and evaluate the influence of these risks on production efficiency. Based on the results of the comprehensive evaluation, logistics evaluation information is generated. This index reflects the efficiency and smoothness of logistics transportation in the production process during the execution of each job scheduling plan. The higher the index, the more efficient the logistics execution, and the lower the index, the more likely there are problems in the logistics execution. The logistics evaluation information provides a quantitative indicator that can intuitively reflect the logistics efficiency of each job scheduling plan. This index helps production managers make choices among multiple job scheduling plans and preferentially select plans with higher logistics execution indexes to improve the overall production line efficiency.

[0076] More specifically, through the simulation analysis of multiple factors such as logistics transportation, accumulation, and interactive influence, the final logistics evaluation information can help production managers accurately select the best job scheduling plan and optimize the production process. The logistics evaluation information can reflect the efficiency of material flow in the production line, reduce problems such as transfer accumulation and interactive influence, and thus improve the overall production efficiency. By predicting and evaluating the logistics execution of different scheduling plans, potential production bottlenecks can be avoided in advance, ensuring the smooth operation of the production line and the effective use of resources.

[0077] Preferably, the device load information and the logistics evaluation information of various job scheduling plans are evaluated to generate a job scheduling plan for scheduling the job units, which comprises the following steps: S51: performing a plan evaluation of the device safety of the job scheduling plan according to the device load information of the job scheduling plan to obtain safety prediction features of each job unit at each future time node corresponding to the job scheduling plan; S52: performing feature form conversion and combination on the safety prediction features of each of the job units at each of the time nodes in the future corresponding to the job scheduling plan, to obtain a safety prediction feature vector matrix of the job units corresponding to the job scheduling plan; S53: summarizing the safety prediction feature vector matrix of each of the job scheduling plans in a matrix vector time sequence change mode, to obtain a safety vector time sequence mode of each of the job scheduling plans, and independently evaluating and interactively matching the safety vector time sequence modes of each of the job scheduling plans, to obtain an independent evaluation value and an interactive matching similarity of the safety vector time sequence mode of each of the job scheduling plans; S54: according to the interactive matching similarity between the safety vector time sequence modes of each of the job scheduling plans, interactively verifying and numerically correcting the independent evaluation values of each of the job scheduling plans, to obtain an interactive evaluation value of each of the job scheduling plans; S55: performing weighted fusion evaluation according to the interactive evaluation value of each of the job scheduling plans and the logistics evaluation information, to obtain a comprehensive evaluation value of each of the job scheduling plans, and taking the job scheduling plan with the highest comprehensive evaluation value as the job scheduling plan; S56: configuring unit performance parameters, unit job tasks and logistics transportation tasks of each of the job units according to the job scheduling plan, so that each of the job units performs job processing on the allocated unit job tasks in the performance mode corresponding to the configured unit performance parameters, and transports the work objects completing the unit job tasks to the designated job units in the next production process link according to the logistics transportation tasks; wherein the unit performance parameters, the unit job tasks and the logistics transportation tasks jointly constitute a job scheduling strategy.

[0078] Specifically, according to the equipment load information of the job scheduling plan, the equipment load conditions of each job unit at each of the time nodes in the future are analyzed. The equipment load graph shows the load conditions of the equipment resources (such as machines, workstations, transmission equipment, etc.) of each job unit in the future period when executing a certain job scheduling plan. According to the equipment load graph, the equipment safety at each of the time nodes in the future is predicted, i.e. whether the equipment will operate in overload, or whether there is a potential risk of equipment failure. These safety prediction features are used to evaluate the equipment load condition of each job unit in a specific time period. Through the safety evaluation of the equipment load, the possible overload or overuse of the equipment can be identified in advance, so as to effectively prevent equipment failure or production stagnation, help to reasonably arrange the load of the equipment, avoid waste or over-reliance on resources, and ensure that the equipment is within a safe working range.

[0079] More specifically, the safety prediction features corresponding to each job unit, such as load, temperature, vibration, and other equipment operating state indicators, are converted into a standardized format suitable for further processing, ensuring that data from different job units can be uniformly processed. The converted safety prediction features are combined to generate a safety prediction feature vector matrix for each job unit, representing the safety state of each job unit at each time node. The form conversion ensures data consistency, making subsequent analysis and evaluation more efficient. By constructing the feature vector matrix, the safety of the equipment can be quantified in multiple dimensions, facilitating further in-depth analysis.

[0080] More specifically, by analyzing the time sequence changes of the safety prediction feature vector matrix of each job unit, the safety vector time sequence pattern under each job scheduling plan is summarized. These patterns represent the trend of equipment load changes over time during the execution of the job scheduling plan. The safety vector time sequence pattern of each job scheduling plan is independently evaluated to determine the safety performance of each job scheduling plan. At the same time, interactive matching analysis is performed to evaluate the similarity of safety vector time sequence patterns between different job scheduling plans. Through the summary of time sequence change patterns, the safety characteristics of different job scheduling plans can be identified, and potential risks or unstable factors can be discovered in advance. Through independent evaluation and interactive matching, a safety evaluation value can be assigned to each job scheduling plan, helping decision-makers choose a safer and more reliable job scheduling plan.

[0081] More specifically, according to the similarity between the safety vector time sequence patterns of different job scheduling plans, the independent evaluation value of the job scheduling plan is interactively verified and numerically corrected. Interactive verification is performed by evaluating the safety performance difference between different job scheduling plans to adjust the initial evaluation value, resulting in a more accurate safety evaluation result. Interactive verification and numerical correction improve the accuracy of the evaluation process through similarity analysis, ensuring that the final evaluation result better reflects the actual safety situation. Through interactive verification, the selected job scheduling plan can effectively reduce risks and improve production stability and reliability.

[0082] More specifically, the interactive evaluation value of each job scheduling plan and the logistics evaluation information are combined for weighted fusion evaluation to obtain a comprehensive evaluation value for each job scheduling plan. This evaluation value considers multiple factors such as equipment safety and logistics efficiency. Based on the comprehensive evaluation value, the job scheduling plan with the highest evaluation value is selected as the final job scheduling plan. Through weighted fusion evaluation, multiple factors such as safety and logistics efficiency are considered to provide a comprehensive decision support tool for managers, ensuring that the selected job scheduling plan has the best overall benefit, with full consideration of equipment safety and logistics efficiency.

[0083] More specifically, according to the selected job scheduling plan, the configuration of the unit performance parameters of each job unit is performed, including the performance mode of the equipment, the work load, the work speed, etc., the corresponding job tasks and logistics transportation tasks are allocated to each job unit, it is ensured that each job unit can efficiently perform the tasks according to the configured performance mode, and the logistics transportation tasks are completed, through the accurate configuration of the tasks and performance parameters of each job unit, it is ensured that each unit can efficiently and accurately complete the work tasks, the production efficiency is improved, through the reasonable task issuing and equipment configuration, the optimal utilization of resources is realized, the waste is reduced and the smoothness of the production process is ensured.

[0084] Preferably, it further comprises performance tracking of each job unit performing the job scheduling strategy according to the equipment load information to obtain a load prediction deviation atlas, and adjusting the job scheduling plan according to the load prediction deviation atlas, and the steps comprise: S61: continuously monitoring the equipment load status of each job unit to obtain an equipment load actual atlas of each job unit, and performing atlas difference analysis on the equipment load information according to the equipment load actual atlas of each job unit to obtain a load prediction deviation atlas; S62: evaluating the equipment safety of each job unit according to the load prediction deviation atlas to obtain the equipment risk characteristics of each job unit continuing to perform the current job scheduling strategy; S63: evaluating the warning threshold of the equipment risk characteristics to obtain the risk warning level of the job unit, and calling the corresponding correction weight from a pre-set database according to the risk warning level, and adjusting the job scheduling plan based on the correction weight.

[0085] Specifically, the equipment load of each job unit is continuously monitored, and real-time equipment operation data are collected, which include the working state, load, running speed, temperature, vibration, etc. of the equipment, through the monitoring data, the equipment load actual atlas of each job unit is generated, which reflects the load condition of the equipment in actual operation, these atlases show the deviation of each job unit in the execution process from the equipment load information, real-time equipment load monitoring ensures that the working state of the job unit can be known at any time, thereby reducing the uncertainty in production, the equipment load actual atlas provides a basis for subsequent atlas difference analysis, and helps to judge whether there is a risk of overload or equipment overload.

[0086] More specifically, the actual load map of each job unit is compared with the preliminary load information to find the differences between the actual load and the predicted load. The difference analysis can help identify whether the device operation meets expectations and reveal possible load deviations. According to the difference analysis results, a load prediction deviation map is generated, which shows the deviation trend between the predicted load and the actual load, reflecting whether the job unit has load exceeding or insufficient conditions. Through the difference analysis of the map, the deviation between the predicted load and the actual load can be clearly identified, helping to discover potential device problems in a timely manner. The load prediction deviation map can provide a basis for further adjustment of the prediction model, thereby improving the accuracy of future load prediction.

[0087] More specifically, based on the load prediction deviation map, the safety of each job unit is evaluated. The safety evaluation takes into account the load condition of the device, the risk of overloading operation, and possible failure conditions. According to the results of the safety evaluation, device risk features are extracted to identify the risks that each job unit may face when continuing to execute under the current job scheduling strategy. For example, overloading operation may cause device failure or production delay. Through device risk feature identification, potential device failures or safety hazards can be discovered in advance to avoid problems during device operation. Real-time evaluation of device safety can dynamically adjust production plans and job scheduling strategies according to actual conditions to ensure the stability of the production line.

[0088] More specifically, the device risk features are evaluated with warning thresholds to set different risk warning levels. According to the severity of the device risk, the device is divided into different risk levels, such as low risk, medium risk, and high risk. According to the risk warning level obtained through evaluation, the corresponding correction weight is retrieved from the pre-set database. Each risk warning level corresponds to a correction weight, and these weights determine the degree of adjustment to the job scheduling plan. Risk warning level evaluation helps managers better understand the risk status of the device and respond quickly. Through the retrieval of correction weights, accurate adjustments can be made according to the actual load and risk status of the device, thereby improving the flexibility and responsiveness of the job scheduling plan.

[0089] More specifically, based on the correction weight retrieved from the database, the current job scheduling plan is adjusted, and the adjustment measures can include optimizing the load distribution of the equipment, rearranging the priority of the job tasks, delaying or advancing the work of certain job units, etc. The adjusted job scheduling plan aims to balance the equipment load, ensure that the equipment will not be damaged due to overload, and maintain the smoothness of the production process. Through adjustment, resource waste can be avoided, and production efficiency can be improved. Through dynamic adjustment of the job scheduling plan, changes in production can be flexibly responded to according to real-time equipment load and safety evaluation results. By adjusting the job scheduling strategy, the equipment load deviation is reduced, the rational use of resources is improved, and thus the production efficiency and equipment utilization rate are improved.

[0090] Referring to Figure 2 In the second aspect, the present application provides an intelligent printing job scheduling device for implementing the intelligent printing job scheduling method of any one of the first aspect, comprising: A model simulation module is configured to collect information of each job unit in the intelligent printing production line, to simulate the intelligent production process condition and logistics transportation relationship of the intelligent printing production line based on the collected information, and to obtain a basic digital model and a deep digital model. A scheme simulation module is configured to collect task target data of the intelligent printing production line, and to simulate a job scheduling scheme of each job unit based on the task target data and the basic digital model, to obtain a plurality of job scheduling plans and equipment load information corresponding to each job scheduling plan. A transportation analysis module is configured to evaluate the logistics transportation execution of each job scheduling plan based on the deep digital model, to obtain logistics evaluation information of each job scheduling plan. A plan execution module is configured to evaluate each job scheduling plan based on the equipment load information and the logistics evaluation information, and to generate a job scheduling plan for job scheduling of each job unit.

[0091] In the present embodiment, the specific implementation of each module in the above device embodiment is described above in the method embodiment, which will not be repeated here.

[0092] In the third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, causes the processor to execute the intelligent printing job scheduling method of any one of the first aspect.

[0093] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent print job scheduling method, characterized by, The method comprises the following steps: information collection is performed on each work unit in the intelligent printing production line to perform digital simulation on intelligent production process conditions and logistics transportation relations of the intelligent printing production line based on the information collection results, so as to obtain a basic digital model and a deep digital model; task target data of the intelligent printing production line is collected, and scheme simulation of unit work scheduling is performed on the task target data based on the basic digital model, so as to obtain a plurality of work scheduling plans and corresponding equipment load information; evaluation is performed on logistics transportation execution of each of the work scheduling plans based on the deep digital model, so as to obtain logistics evaluation information of each of the work scheduling plans; evaluation is performed on the equipment load information and the logistics evaluation information of each of the work scheduling plans, and a work scheduling plan is generated to perform work scheduling on each work unit.

2. The intelligent print job scheduling method of claim 1, wherein, The information collection on each work unit in the intelligent printing production line comprises the following steps: production application, work performance and site layout information of each work unit in the intelligent printing production line is collected, so as to obtain production process information, work performance information and location information of each work unit.

3. The intelligent print job scheduling method of claim 2, wherein, The digital simulation on intelligent production process conditions and logistics transportation relations of the intelligent printing production line based on the information collection results comprises the following steps: a work simulation model of each work unit is generated according to each work performance information, and each work simulation model is associated and connected based on intelligent production process conditions according to each production process information, so as to obtain a basic digital model; each work simulation model is associated and connected based on logistics transportation relations according to the location information of each work unit, so as to obtain a deep digital model.

4. The intelligent print job scheduling method of claim 3, wherein, The task target data of the intelligent printing production line is collected, and scheme simulation of unit work scheduling is performed on the task target data based on the basic digital model, so as to obtain a plurality of work scheduling plans and corresponding equipment load information of each work scheduling plan. The steps comprise: each item of production task information to be executed by the intelligent printing production line and a priority weight corresponding to each item of production task information are collected as task target data; task allocation data of each link of the production process is obtained by performing task allocation of each link of the production process on the task target data based on the basic digital model; analysis of data correlation of the task allocation data of each link of the production process is performed, so as to obtain work pressure information of each link of the production process of the intelligent printing production line; corresponding specific work task information is generated for each work simulation model in the basic digital model according to the work pressure information of each link of the production process; a plurality of work scheduling plans are generated in combination with the priority weight corresponding to each item of production task information and the specific work task information; historical data of equipment load states of each work unit is collected, and equipment load conditions of each work unit are simulated and deduced according to the historical data of each work unit based on each of the work scheduling plans, so as to obtain equipment load information of each of the work scheduling plans.

5. The intelligent print job scheduling method of claim 4, wherein, The step of evaluating logistics transportation execution of various job scheduling plans according to the deep digital model includes: The step of simulating logistics transportation conditions of the job scheduling plan according to the deep digital model includes: The step of simulating and evaluating the interaction between logistics accumulation and logistics transportation of each link of the production process based on the logistics characteristics of each job unit includes:

6. The intelligent print job scheduling method of claim 1, wherein, The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes:

7. The intelligent print job scheduling method of claim 1, wherein, The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes:

8. An intelligent print job scheduling apparatus characterized by comprising: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the logistics evaluation information of various job scheduling plans includes: The step of generating a job scheduling plan for the job scheduling plan based on the equipment load information and the A scheme simulation module is configured to collect task target data of the intelligent printing production line, and perform unit job scheduling scheme simulation on the task target data according to the basic digital model to obtain a plurality of job scheduling plans and equipment load information corresponding to each of the job scheduling plans. A transportation analysis module is configured to perform logistics transportation execution evaluation on each of the job scheduling plans according to the deep digital model to obtain logistics evaluation information of each of the job scheduling plans. A plan execution module is configured to perform plan evaluation on the equipment load information and the logistics evaluation information of each of the job scheduling plans, and generate a job scheduling plan to perform job scheduling on each job unit.

9. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and the computer program causes the processor to perform the intelligent printing job scheduling method according to any one of claims 1-7 when the computer program is run by the processor.

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