533 results about "Production planning" patented technology

The invention relates to an impregnated paper production process multi-parameter collaborative optimization control method based on reinforcement learning, and the method comprises the following steps: S1, building an omnibearing data collection network, achieving the panoramic perception of a production process, and obtaining a real-time data flow; s2, based on a data fusion technology, performing intelligent integration on different sources and different types of data to obtain a fused data set; s3, according to the fused data set and based on a process mechanism, constructing a knowledge graph of the impregnated paper production process; s4, constructing a digital twinborn model of the impregnated paper production line in combination with the fused data set and the knowledge graph; and S5, designing a multi-objective optimization decision framework according to the digital twinborn model, the production plan and the constraint condition, and realizing collaborative optimization of different time scales. According to the invention, multi-parameter collaborative optimization control of the impregnated paper production process is realized.

Disclosed in the present invention is a material picking method based on a mobile robot, comprising the following steps: step S1, acquiring a real-time production plan and a bill of materials, wherein the bill of materials comprises material codes, material names, material quantity and lead time; step S2, on the basis of the material codes, dividing the bill of materials into a plurality of task units suitable for single operation of the mobile robot, and assigning corresponding picking stations; step S3, performing picking state labeling on the task units on the basis of the lead time of materials in the task units, and assigning corresponding mobile robots on the basis of the labeled picking states; and step S4, the mobile robots performing path planning on the basis of the assigned task units, and executing the assigned task units on the basis of the planned path, so that racks to which the materials in the assigned task units belong are transported to specified picking stations for operators to pick the required materials.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention provides an MES-based solar cell production and manufacturing execution method and system, and the method comprises the steps: collecting equipment states and technological parameters in real time, combining a bar code system to achieve the single-chip-level metadata association, constructing a standardized data architecture, building a multi-dimensional index, converting the expert experience into reasonable rules, and carrying out the calculation of the reasonable rules. The method comprises the following steps: simulating the influence of parameter combination on product performance, triggering an edge computing node to call a knowledge graph to perform parameter optimization and production simulation based on a dynamic threshold early warning mechanism, identifying process improvement opportunities through a batch tracing report and clustering analysis, forming knowledge base iteration update, predicting inventory risks in combination with a production plan and equipment load, and performing production management. According to the method, intelligent purchasing is automatically triggered, raw material distribution is optimized, technological parameter self-optimization, abnormity self-healing, quality traceability and supply chain collaboration are achieved through data driving, the production efficiency, the product consistency and the resource utilization rate are improved, and a whole-process digital solution is provided for photovoltaic manufacturing.

The invention discloses a product production process scheduling method and system based on intelligent manufacturing, and belongs to the technical field of intelligent manufacturing. According to the method, rapid local rescheduling is realized through fault influence domain identification, a three-level response strategy and real-time equipment health assessment. When an equipment fault signal is detected, a to-be-executed task on the fault equipment is identified, a subsequent task chain is traced through the technological process dependency relationship, the boundary of a fault influence domain is determined, and the influence domain relates to 10%-20% of tasks in the whole production plan. And selecting a corresponding response strategy according to the predicted repair time, adopting a task delay strategy when the predicted repair time is less than 30 minutes, performing local redistribution on tasks in the influence domain when the predicted repair time is less than 30 minutes to 2 hours, and expanding the influence domain to the whole process for comprehensive optimization when the predicted repair time is more than 2 hours. Meanwhile, monitoring data such as equipment vibration, temperature, current and rotating speed are collected to calculate a health degree score, and 5%-10% of productivity redundancy is reserved for high-risk equipment.

The invention relates to a steel production-oriented multi-base multi-production-line multi-process global plan optimization method and system. The method comprises the following steps of: receiving and standardizing order demand data; executing hierarchical processing and distribution of multi-base orders, and outputting a base-level order set; deducting and marking the net production demand quantity of the base-level orders; executing multi-production-line order allocation in a single base, and generating a production line level order set; performing cross-process collaborative batch grouping on the production line level order set based on steel making-continuous casting-rolling-post-processing process constraints to generate a cross-process batch plan; constructing a global evaluation model, performing comprehensive evaluation on an intermediate result, and generating a parameter correction instruction based on an evaluation result for adjusting parameters, constraints or distribution strategies of subsequent modules; and finally, generating a production plan scheme with consistent cross-base, cross-production-line and cross-process, and triggering rolling optimization update when disturbance occurs. According to the invention, unified coordination and global optimization of multi-module plan results are realized.

The invention discloses a self-adaptive scheduling method and system for multi-process production, and the method comprises the steps: generating a production plan according to basic data of a multi-process production task, executing the plan, monitoring a production field data flow in real time, and when an abnormal event is monitored, extracting abnormal event information including an abnormal source, abnormal occurrence time and predicted duration; inputting the abnormal event information into a preset rule engine, positioning an influenced scheduling block set according to a preset influence evaluation rule, marking the influenced scheduling block set as a to-be-rearranged task, and outputting the to-be-rearranged task; local rescheduling optimization is carried out in the affected time-resource range, and candidate rescheduling schemes are generated; and screening out an optimal scheme from the candidate rescheduling schemes, updating and integrating the optimal scheme into the production plan, and automatically updating the associated productivity load analysis data. According to the method, the scheduling block set influenced by abnormity can be accurately positioned, local rearrangement optimization is carried out, and the stability of a production plan is guaranteed on the basis of low computing power consumption.

The invention provides a carbon energy efficiency dual-objective optimization analysis method and system based on digital twinning, and relates to the technical field of carbon energy efficiency optimizing.The method comprises the steps that simulation modeling is conducted on an air compressor system and a sterile cold filling line of a beverage factory based on digital twinning, a filling twinning simulation space is constructed, and a production planning system is accessed; a bottle blowing machine beat signal, filling bottle attribute characteristics and filling bottle sterile blowing requirements in a preset time zone are obtained through a production planning system; in the filling twinborn simulation space, with minimization of carbon emission intensity and maximization of production stability as double optimization targets, iterative optimization search is conducted on the air compression control scheme of the air compressor system, and the optimal air compression control scheme is output; and the filling operation in the preset time zone is executed according to the optimal air pressure control scheme. The technical problems that in the prior art, an air compressor system adopts a fixed high-pressure control strategy, dynamic management and control cannot be conducted according to actual production requirements, and energy consumption is wasted are solved.

The invention relates to the technical field of manufacturing execution systems, in particular to an intelligent scheduling method integrating demand prediction and supply matching, which comprises the following steps of: acquiring to-be-scheduled order information and a workshop real-time resource state in a manufacturing execution system, constructing an order demand feature vector, the method comprises the following steps: acquiring an equipment precision retentivity value, a current load rate value, an energy consumption level value and an operator skill proficiency value, and constructing a resource supply capability vector; according to the method, when an urgent order insertion instruction is received, a clear decision basis is provided, a rigid scheduling locking block which must be kept unchanged and an adjustable liquid adjustable scheduling block which can be adjusted can be distinguished, only the latter is subjected to accurate splitting and backward moving operation, and the target-clear local adjustment mode is adopted, so that the operation efficiency is greatly improved. The huge calculation overhead and the violent fluctuation of the production plan caused by the traditional global rescheduling are avoided, and the agility of the manufacturing system to deal with the market change is enhanced.

The invention provides a tire factory multi-dimensional dynamic collaborative production command method and system, and relates to the field of industrial manufacturing informationization, and the method comprises the steps: collecting equipment layer sensor data, MES system production plan data and quality detection system data in real time through a protocol conversion module, and extracting the input of a frequency domain feature process conduction matrix; the method comprises the following steps: constructing a process conduction matrix of banburying, forming and vulcanizing, inputting current process abnormal data containing a vibration spectrum abnormal value into the process conduction matrix, outputting a prediction index set, and dynamically correcting a process alarm threshold value through an LSTM model based on the prediction index set output by the process conduction matrix, environment temperature and humidity and an equipment aging coefficient. A parameter adjustment control is embedded in the visual billboard, the timeliness and accuracy of a production plan can be improved according to comparison of a corrected process alarm threshold value and actual production data, the labor and time cost is reduced, comprehensive control of real-time data related to production is achieved, and the production efficiency and quality are improved.

The invention discloses an LCD display screen production multi-workshop collaborative management method based on AI prediction, and particularly relates to the technical field of production management. The method is used for solving the decision difficulty that an existing production management system is difficult to quantitatively evaluate process interruption loss and order delay loss when dynamic demands change. The method comprises the following steps: acquiring production data, order information and material commitment data of each workshop, respectively predicting first loss of interrupting a current irreversible process and second loss of delaying a new order by using an AI prediction model, correcting the first loss in combination with the idle cost of downstream key resources, generating an advantage and disadvantage balancing index based on the corrected loss value, and calculating the defect of the new order according to the advantage and disadvantage balancing index. And constructing a multi-workshop topology network model to simulate disturbance propagation and identify an optimal decision point, and finally generating an adjusted production plan and outputting the adjusted production plan to a production management system to realize multi-workshop collaborative optimization management.

The invention discloses a steel production line dynamic scheduling system based on data integration analysis, which belongs to the technical field of steel production line plan scheduling, and is characterized in that a dynamic knowledge graph driven by integrated multiple models is constructed, and scattered and heterogeneous data acquired in real time is converged into a data stream; an initial scheduling scheme is produced based on original order data and production line resource data in the'data stream ', simulation deduction is performed based on digital twin driving, an optimized planned production list is obtained, production is executed with the planned production list as a reference, real-time production data and change order data in the real-time'data stream' are received, and the real-time production data and the change order data in the real-time'data stream 'are obtained. Process deviation and order change in the actual production process are dynamically responded, correlation root cause analysis and scheduling decision making are carried out on multiple disturbance factors based on model and map cooperation, dynamic scheduling based on priority and resource constraint is achieved, it is ensured that the actual production condition is consistent with a production plan after scheduling optimization, and production flexibility and adaptability are improved.

The invention provides a chemical industry-oriented multi-scale multi-dimensional intelligent production scheduling method and system, and the method comprises the steps: obtaining various production scheduling related data based on a factory model, a production management system, an inventory management system, a sales management system and an equipment management system which are established in advance; performing production scheduling according to the various production scheduling related data, a predetermined production quantity standard and gross profit data of different products, and determining a production plan of a target time granularity; and carrying out production according to the production plan, obtaining order insertion data and / or production abnormal information in real time, and updating the production plan. At least efficient, collaborative and benefit-maximized production scheduling can be realized, multi-objective optimization, dynamic order insertion, multi-system data integration and real-time collaboration are supported, and the actual demand of the fine chemical industry field for efficient production scheduling is met.

The invention discloses a pet food intelligent production management and control system and a traceability code spraying device thereof, and relates to the technical field of pet food production, and the pet food intelligent production management and control system comprises a production planning unit, a raw material management unit, a production control unit, an intelligent mistake proofing unit and a quality traceability unit. The production planning unit optimizes a production plan and accurately allocates resources, the raw material management unit performs supplier qualification evaluation, raw material multi-level quality inspection and intelligent inventory regulation and control, quality is controlled from the source, the supply chain risk is reduced, the production control unit depends on the equipment real-time monitoring and fault early warning technology, the stability of production parameters is guaranteed, and the production efficiency is improved. The intelligent error-proofing unit integrates OCR verification, threshold detection and prediction model construction, rejects error detection data, prevents data missing, realizes real-time early warning and closed-loop processing of quality risks, and eliminates quality problems in the bud stage, and the quality tracing unit performs real-time early warning and closed-loop processing on the quality risks through full-link data acquisition and a unique tracing code. And accurate positioning and rapid recall of problem products are supported.

The invention discloses an induction cooker production line productivity prediction system using big data analysis, and relates to the technical field of industrial Internet of Things, the induction cooker production line productivity prediction system comprises a production line management center, the production line management center is in communication connection with the following modules: a multi-source data acquisition module used for acquiring full-link data from each link of an induction cooker production line, carrying out preprocessing to form a full-link data sequence table; and the digital twinning virtual mapping engine is used for constructing a virtual model of the induction cooker production line in combination with a digital twinning technology. The real state of the production line can be reflected in real time by collecting the equipment operation state, the production progress and the material inventory data of the induction cooker production line in real time and combining with the virtual model constructed by the digital twin technology, so that the system can capture abnormities in the production process in time, accurate productivity prediction is carried out, and the production efficiency is improved. The problem that productivity adjustment is not timely due to information lag is effectively avoided, and the accuracy and flexibility of a production plan are remarkably improved.

When a trigger event causing the priority change of the production task is recognized, the influence degree of the trigger event on an overall production plan is evaluated, whether the influence degree of the trigger event on the overall production plan is low or not is judged, and when the judgment result is no, the priority of the production task is changed according to the priority of the production task. Generating a plurality of candidate priority change schemes, performing priority change cost evaluation on the candidate priority change schemes, and determining a target priority change scheme in the candidate priority change schemes according to a priority change cost evaluation result, the priority of the production task is adjusted according to the target priority change scheme, the optimal production priority change scheme can be produced in combination with the actual situation, and the adverse effect caused by production priority change is effectively reduced.

The invention discloses a production material accurate scheduling method and system under cooperation of WMS and MES, and relates to the technical field of material scheduling. Comprising a data acquisition and processing module which acquires data and preprocesses the data; the material demand planning module is used for constructing a material demand prediction model by using a time sequence analysis method, and decomposing a high-level production plan into specific material demand lists step by step according to a process route and process requirements; and the inventory management module is used for managing the materials, and dynamically distributing and adjusting the storage locations by adopting a clustering algorithm according to the characteristics of the materials and the layout of the warehouse. According to the invention, based on linkage of the WMS and the MES, full-chain closed-loop management from planning to execution is realized, and the cross-system cooperation efficiency is improved; a dynamic demand prediction system is constructed by adopting time sequence analysis, a regression model and a machine learning algorithm, a historical rule and market changes are considered, and the prediction precision is improved.

A management device according to the present invention comprises a control unit. The control unit executes processing that acquires resource information that stipulates remaining amounts of resources needed to process a workpiece for respective processing systems, processing that, when input of a production plan that stipulates at least a type of workpiece to be produced and a quantity for the workpiece to be produced has been received, allocates processing of the relevant quantity of the workpiece to a plurality of processing systems and transmits a processing order that designates the type of workpiece to be processed and the quantity of the workpiece to the processing systems that are to do the processing, and processing that determines whether the resources at the processing systems that are to do the processing are insufficient on the basis of the processing order and the remaining amounts of the resources at the processing systems that are to do the processing and, when it has been determined that the resources are insufficient, outputs a warning that indicates that the resources are insufficient.

The invention relates to the technical field of production management, and discloses a material data change management method, device and equipment and a medium which are applied to a manufacturing execution system.The method comprises the steps that planned order material data of a current vehicle is acquired; based on the planned order material data, determining a material use quantity required by production of each station, and eliminating missing material data to generate processed material data; comparing the plan order material data with the processed material data to generate a loading difference list according to a comparison result; and updating the planned order material data according to the loading difference list to obtain target order material data. According to the technical scheme provided by the invention, accurate material breakpoint control can be realized, so that material change, trial assembly and production plans are kept consistent.

The invention relates to the technical field of path planning, and particularly provides an unmanned mine car scheduling method, system and device based on a real-time situation, and a medium, and the method comprises the steps: receiving real-time situation sensing data, early warning information and a production plan of a mining area; generating a task template according to the production plan, instantiating the corresponding template into a to-do task when an excavator is ready, and adding the to-do task into a task pool; processing tasks in the pool according to the early warning information, including pausing or removing fault equipment associated tasks, or adjusting priorities and destinations of tasks influenced by risks; in each scheduling period, calculating a task dynamic priority based on a task pool, real-time data, early warning information and a planned progress, allocating mine cars and excavators to tasks and planning paths according to the task dynamic priority, performing conflict prediction and analysis on the allocated tasks and paths, and adding conflict-free tasks into a feasible instruction set; and finally issuing an instruction set to the corresponding equipment. Vehicle collision and resource scrambling can be effectively avoided, and operation safety is guaranteed.

The invention relates to the technical field of concrete production, in particular to an intelligent concrete production management system and method. The method comprises the following steps: acquiring multi-source data, and carrying out accurate prediction on concrete demands; generating a dynamic production plan of concrete, and optimizing the production plan; an intelligent vehicle scheduling system is constructed, and real-time optimization of a dynamic path is carried out; the production process of the concrete is intelligently monitored, and the quality is dynamically regulated and controlled; the whole concrete transportation process is monitored in real time; through the above mode, accurate prediction of the concrete demand can be realized, the situation of unreasonable production plan arrangement is avoided, and the project progress and the enterprise economic benefit are ensured.

The invention discloses an intelligent warehouse logistics management system and method based on a mobile robot, and relates to the technical field of warehouse logistics, and the system comprises a warehouse-in management module, a warehouse-out management module, an operation management module, an inventory management module, a report management module, a task management module and a log management module. The warehousing management module comprises an arrival list sub-module, a production plan list sub-module, a red character material warehouse-out sub-module and a manual warehousing list sub-module; the warehouse-out management module comprises a material picking application list sub-module, a delivery list sub-module, a manual warehouse-out list sub-module and a sales order sub-module; and the operation management module comprises a spot receiving operation sub-module, a sampling inspection confirmation sub-module, an inspection submodule, a quality inspection operation sub-module, a warehouse returning operation sub-module, a racking operation sub-module, a goods picking operation sub-module and a warehouse moving operation sub-module. Automatic storage and taking of the robot are achieved, the material carrying accuracy and efficiency are improved, and line side warehouses and three-dimensional warehouses are dynamically managed. And cost reduction and benefit increase of enterprises are realized.

In an approach for improving operational efficiencies in IoT agricultural ecosystems, a processor receives a request from a user for a production plan to modify one or more values of a set of values of an area of interest. A processor maps the area of interest using a set of data originating from a LIDAR instrument to create a digital elevation model of the area of interest. A processor creates a knowledge set to train a decision engine. A processor calculates one or more modifications to be made to the one or more values of the set of values of the area of interest. A processor generates a map of applicable land options that the user may interact with to select the one or more modifications to be made to the one or more values of the set of values. A processor plots a finalized rendering of the production plan.

The invention belongs to the technical field of automobile manufacturing, and particularly discloses an intelligent flexible production scheduling method and system for automobile orders, and the method comprises the following steps: collecting order demands of a user; according to the order delivery time requirement of a user, reversely deducing production plans of a coating workshop and a welding workshop by taking the general assembly workshop as a starting point to obtain a production plan of a production line; according to the demand within a period of sales, in combination with the material arrival rhythm, the workshop capacity and the sales demand successive date factor, daily production vehicles are discharged, and according to the selected daily production vehicle distribution result and the final assembly lean production proportion requirement, constraint conditions are set, and coating production sorting and scheduling calculation are carried out; and on the basis of the scheduling, a welding vehicle queue plan is generated in combination with a coating workshop production plan in a user order demand. By adopting the technical scheme, production scheduling is carried out by using a production scheduling reverse pushing mode based on the production characteristics of the automobile orders, flexible production scheduling of the automobile orders is realized, and the efficiency is improved.

The invention discloses a product quality tracing method for fish flour-wrapped products, and belongs to the technical field of product quality tracing, and the method specifically comprises the steps: extracting a production order of the fish flour-wrapped products; searching historical quality data and external factor data of the fish wrapped flour product according to the raw material batch identifier and the production time identifier of the production order; based on the historical quality data and the external factor data, a quality risk prediction model is constructed, and the model outputs defect probability and key influence factors; monitoring a real-time production process of the fish flour-wrapped product, wherein the real-time production process comprises raw material input parameters, processing temperature parameters and equipment operation parameters; according to the quality risk prediction model and the real-time production flow data, dynamically adjusting a production plan, modifying process parameters or redistributing production resources; and integrating the prediction result and the production flow data to generate a quality tracing report of the fish flour-wrapped product, including a risk early warning identifier and a production optimization suggestion.

The invention relates to the technical field of intelligent battery manufacturing, and discloses an intelligent battery automatic manufacturing system and a management system. A raw material acquisition module of the system obtains battery manufacturing raw material data in an initial time period and converts the battery manufacturing raw material data into a standardized format; after the intelligent analysis module receives the standardized data, physical and chemical characteristics of the raw materials are analyzed through a deep learning model, and a characteristic analysis report is generated; the production planning module determines production parameters according to the report and generates a production instruction; the execution control module controls production equipment to complete battery automatic manufacturing based on the production instruction; the quality detection module detects the battery after being manufactured and generates quality detection data; and the feedback optimization module adjusts deep learning model parameters according to the quality detection data, and optimizes a subsequent production instruction generation process. According to the system, intelligent closed-loop management of battery manufacturing from raw material analysis to production optimization is realized through cooperation of all the modules.

The invention discloses an AGV scheduling system and scheduling method, relates to the technical field of flexible workshop job scheduling, and solves the problems that local optimization damages global circulation efficiency, characterization defects and invalid actions frequently occur and robustness is poor in the prior art. The system comprises a manufacturing execution system, a data acquisition module, a decision-making system and an environment interaction module, the manufacturing execution system decomposes a production plan into discrete manufacturing tasks and stores the discrete manufacturing tasks in a dynamically updated task pool, a workshop environment generates state information in real time, the state information comprises environment dynamics and AGV instant observation data, and the AGV real-time observation data is transmitted to the manufacturing execution system. The decision-making system adopts a neural network model based on multi-agent reinforcement learning, multiple groups of candidate scheduling strategies are generated by processing real-time observation data, the environment interaction module is responsible for verifying feasibility verification and performance evaluation of actions, iterative updating of the system state is achieved by generating observation values, and the decision-making system is in real-time scheduling. The modular design ensures the real-time performance and adaptability of the scheduling decision.

The invention discloses a petrochemical device multi-level production plan cooperation method and system, and relates to the technical field of production plan cooperation methods, and the method comprises the steps: building a dynamic elastic productivity model based on historical operation data and current working condition parameters of a petrochemical device, and outputting an adjustable productivity boundary of the petrochemical device; inputting the adjustable capacity boundary as a constraint condition into long-term, middle-term and short-term production plans, and outputting load plans of each level; calculating a load fluctuation difference between the load plans of each level, and outputting a collaboration degree index; comparing the collaboration degree index with a preset threshold value, when the collaboration degree index does not reach the standard, introducing a collaboration constraint in multi-objective optimization, readjusting load distribution of each level, and outputting a production plan scheme; and executing the production plan scheme, collecting actual load data, calculating the deviation between the plan and the reality, outputting an execution deviation result, judging whether the allowable range is exceeded or not according to the execution deviation result, and if so, generating a corresponding correction amount and feeding back the correction amount to the medium-term plan model.

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.

The invention discloses a smart factory intelligent production scheduling system for a complete set of products, and relates to the technical field of the manufacturing industry. By establishing the product model, the production line model and the work station model and combining the work calendar, production order synchronization, special process treatment and a semi-finished product pulling production scheduling algorithm, scientific modeling and intelligent production scheduling of a complete set of product production plan are realized, the accuracy and the performability of the production scheduling plan can be improved, smooth production according to the delivery time is ensured, and the production efficiency is improved. Meanwhile, multiple production lines, multi-level plans and father-son order management are supported, and the production efficiency, the material neat rate and the flexible coping capacity of a factory are improved.