87 results about "Production schedule" patented technology

The invention relates to the technical field of intelligent manufacturing and production optimization scheduling, and particularly discloses a thermal fuse flexible production-oriented order scheduling resource scheduling optimization method, which comprises the following steps of: generating a structured vector by extracting inherent and dynamic characteristics of an order, and constructing a production scheduling dynamic environment model by fusing a device resource state time sequence; the order comprehensive process switching cost and the resource constraint tightness are calculated, order clusters are divided through multi-target constraint clustering, and a pre-arrangement sequence is generated; key resource conflict early warning is completed through time sequence prediction; constructing a multi-target scheduling model by taking the order cluster as a unit, and generating a look-ahead window scheme by combining a rolling optimization solidification plan baseline; and finally, on the basis of a real-time execution data updating model, through dynamic priority preemption and local rescheduling adjustment when abnormity occurs, the problems of high cost of small-batch mixed arrangement, conflict identification lagging, easy damage of a thermosensitive process steady state and poor compliance constraint adaptation are solved, and collaborative optimization of the scheduling efficiency, the production yield and the compliance is realized.

The invention relates to the field of mine production scheduling, and discloses a mine production scheduling auxiliary method and system based on a knowledge graph. According to the method, a cue word template is constructed, and a large model is guided to construct structured data subjected to data purification, so that the reliability of knowledge graph data construction is improved. According to the method, accurate analysis and purification of entities, attributes and time sequences are achieved in multi-source data, the structuralization and accuracy of knowledge extraction are improved, feature enhancement and triple optimization are achieved through prompt word semantic constraint and a contrast learning mechanism, and the real-time performance and reliability of mining field knowledge graph construction are greatly improved. The method still has high adaptability and expansibility under the condition of few samples, intelligent decision making and dynamic optimization of mine production scheduling can be effectively supported, and the knowledge graph construction efficiency and application value are remarkably improved.

The invention relates to the field of production scheduling, in particular to an intelligent scheduling method and system for towel production plans. The method comprises the following steps: obtaining towel production order data and a process execution sequence of a towel production information platform, constructing a dependency relationship between process task nodes and tasks according to the process execution sequence and the towel production order data, and generating a towel process task chain set; analyzing resource occupation overlapping conditions among the towel process task chains to construct conflicting resource nodes, analyzing the conflicting resource nodes, constructing conflicting paths, generating a conflicting chain graph, and dividing conflicting release priority areas; analyzing the towel process task chain based on the conflict release priority area and the conflict chain atlas, generating an optimized task chain, and obtaining a schedulable task chain and a path adjustment record; and updating the process task node through the path adjustment record, and generating a towel scheduling table and a towel scheduling structure chart. The method can improve the reasonability of towel production resource scheduling.

The invention relates to the technical field of industrial information processing of production logistics scheduling, in particular to an intelligent material scheduling decision-making method and system based on a large language model, and the method comprises the steps: obtaining historical scheduling data, a production plan text, real-time material state information, production rhythm information and production main data; constructing a time statistical model and analyzing the plan text to obtain a scheduling constraint set; generating a candidate transfer time window after consistency verification, and determining a target time window; and generating a timestamp sequence, aligning and correcting according to rhythms, and outputting a scheduling instruction sequence. Constraint conflicts and non-executable instructions can be reduced, and delivery and beat stability is improved.

The invention discloses a production element demand-driven process collaborative modeling method. The method comprises the steps of defining a relationship and a hierarchical structure between labels by using an OWL, converting a process element rule into a label combination which can be understood by a machine, performing element matching by adopting an intelligent algorithm, and maximizing a matching degree between a production demand and an information model; conflict detection and model release: detecting and eliminating time conflicts and capability conflicts, and finally releasing a conflict-free matching model to ensure optimal configuration and efficient utilization of resources; when a process rule or an information model used in the production schedule is edited or deleted, the timed task is triggered to check whether the associated production task is completed or not; and meanwhile, due to the introduction of a version chain mechanism and a rollback function, the traceability and the restorability of the change are ensured. According to the method, full-process automation of process requirement definition, resource capacity matching and production scheduling plan conflict inspection and elimination is realized, and the problem of low collaboration efficiency caused by model splitting is eliminated.

This invention discloses a dynamic scheduling method and system for precast concrete beams to meet the demand for beams from multiple construction sections. The method includes: S100, mapping the beam storage platforms to a spatiotemporal grid with turnover attributes, and converting the beam erection demand from multiple construction sections into a standardized production work order pool; S200, clustering all work orders to be produced based on a mold family library to generate an initial continuous production sequence that prioritizes reducing the frequency of mold disassembly and assembly and adjustment time; S300, verifying the platform occupancy of the initial sequence based on curing cycle parameters, and automatically inserting production intervals or adjusting the sorting when spatiotemporal conflicts are detected to correct the production schedule; S400, applying a weighted scoring model to quantitatively sort each plan, and locking the plan with the highest comprehensive score as the current execution target; S500, decomposing the final plan into mold modification, pouring, and beam moving operation instructions accurate to the hour and issuing them. Through multi-plan comparison, the ability of precast beam plants to fulfill complex beam supply demands is improved.

The invention provides an intelligent production scheduling and load balancing method and system for a silicone rubber production line, and relates to the field of silicone rubber production and manufacturing, and the method comprises the steps: obtaining production task data and equipment state data, constructing a dual prediction space containing an instant state space and an expected state space, and calculating the instant state and expected state prediction values of a station. And combining to obtain a station load saturation degree to generate a load distribution diagram. And generating an initial production scheduling sequence based on the production task data and the load distribution diagram, converting inter-station material transmission time and load saturation into a directed flow diagram, determining a task allocation boundary by calculating a maximum flow-minimum cutting set, and performing grouping reconstruction on the initial production scheduling sequence to obtain an optimized production scheduling sequence. And finally, superposing a material heat preservation time constraint, calculating an optimal task flow path to carry out time sequence adjustment, and generating a final production scheduling scheme. The production efficiency of the silicone rubber production line can be effectively improved, equipment load balance is achieved, and the product quality stability is guaranteed.

The invention relates to the technical field of high-end equipment manufacturing, and discloses an intelligent production line for new energy automobile support part manufacturing and an operation method and application of the intelligent production line. The production line comprises a storage and dispatching unit, an AGV logistics and transfer unit, a 3D visual recognition and positioning unit, a self-adaptive intelligent clamping unit, a multi-axis numerical control machining center unit and an online detection and quality feedback unit. The production line carries out global optimization production scheduling through a central scheduling model, rapid remodeling and accurate clamping are achieved through 3D vision and a flexible clamp, parameters are adjusted in real time through a self-adaptive model according to force and vibration signals in the machining process, and a quality closed loop is formed through online detection. The problems of frequent die change, low efficiency and poor precision consistency in a multi-variety small-batch production mode are effectively solved, high-degree flexible production is realized, the production efficiency and the product quality stability are remarkably improved, and the dependence on manpower is reduced.

The invention relates to a data analysis method based on a supply chain, in particular to the technical field of business management, and the method comprises the following steps: S1, collecting supply chain data from an internal system and an external source, the internal data comprising a production plan, a production schedule, an inventory level, a purchase order and a sales record, the external data comprises logistics transportation information, market demand indexes, consumer behavior characteristics, meteorological monitoring data, road traffic flow and policy and regulation change information; according to the invention, by establishing a multi-source data access channel, data of an ERP system, an MES system, a WMS system, an external data platform and an Internet of Things device are uniformly collected, and historical data and real-time data are combined to construct a multi-dimensional time sequence database, so that complete acquisition and structured storage of supply chain global information are realized; and the comprehensiveness and accuracy of data input are ensured.

This invention discloses a production data-driven intelligent scheduling method and system for mechanical equipment, relating to the technical field of equipment scheduling. The method includes: constructing a set of equipment operating characteristics; comprehensively evaluating the current load rate, processing capacity, and failure risk of each piece of mechanical equipment; constructing a scheduling objective function using equipment survivability scores as scheduling decision weight parameters; performing task allocation and processing path optimization calculations to generate an intelligent scheduling scheme for the mechanical equipment; and monitoring the scheduling execution process in real time to adaptively adjust the production scheduling scheme. This invention solves the technical problems in existing technologies where static scheduling cannot adapt to real-time changes in equipment status and lacks a dynamic risk feedback mechanism, leading to equipment overload, unplanned downtime, and cascading production delays during scheduling scheme execution. It achieves the technical effects of improving the production scheduling system's adaptability to dynamic equipment operating conditions, reducing the risk of unplanned downtime, and optimizing equipment utilization and production delivery reliability.

The invention belongs to the technical field of scheduling optimization, and discloses a manufacturing industry advanced plan scheduling system and method based on artificial intelligence, and the method comprises the steps: generating an initial scheduling scheme according to production order data and manufacturing resource constraint data; analyzing the initial scheduling scheme to establish a basic time sequence graph, and performing man-hour probability distribution fitting on each task node based on historical production data to obtain a probability time sequence graph; and traversing each task node, calculating and correcting the material ready time based on the man-hour probability distribution function of the previous task node and a preset on-time completion confidence coefficient, updating the plan time of each task node, and obtaining a final production schedule. Adjacent processes form a parallel overlapping relation by starting a time delay mechanism, so that the production cycle is effectively shortened; and meanwhile, the planned time is dynamically adjusted based on the man-hour probability distribution, the time margin can be reserved according to the difference of the actual fluctuation characteristics of each process, and the robustness of the scheduling scheme is improved.

The application relates to the technical field of cloud-edge collaboration, in particular to a production scheduling intelligent management and control system based on cloud-edge collaboration, which comprises a task characteristic aggregation module, which extracts a task type code based on an input task request, queries a task type and calculation amount mapping table in a pre-stored knowledge base, matches the task type and extracts a calculation amount parameter. In the application, the task data packet size, the real-time requirement threshold and the bandwidth benchmark value are linked to calculate the transmission time consumption parameter by dynamically matching the task type and the pre-stored calculation amount mapping relationship, the quantitative mapping of task demand and resource supply is realized, and the parameter deviation caused by traditional artificial experience estimation is eliminated. The CPU occupancy rate value of the edge node process queue is collected in real time, the multi-dimensional node state vector is generated by combining the cloud virtual machine instance quantity and the memory residual quantity data, the limitation of traditional single resource index monitoring is broken through, and the time and space fine granularity of load evaluation is improved.

A smart factory energy efficiency optimization system and method based on digital twin technology. The system comprises a smart factory visualization module, an energy consumption preprocessing module, an energy consumption prediction module, and energy efficiency control module. A predicted operating time of a device for the day is obtained on the basis of a production speed of the device and a production quantity for the day in a real-time production schedule plan; recovered energy of the device for the day is predicted on the basis of the predicted operating time; the recovered energy for the day is then added to original stored energy in an energy recovery system to obtain actual available energy that the energy recovery system can provide for the day; the actual available energy is compared with tiered energy consumption of tiered devices for the day; and corresponding control signals are obtained on the basis of a comparison result. Therefore, recovered energy can be supplied to the tiered devices, maximizing energy utilization efficiency while minimizing the number of times of switching an energy supply mode, thus ensuring the stability of energy supply during operation of a production device.

This invention discloses a visualized intelligent scheduling system for SMT production lines, relating to the field of precision electronics. It solves the problem of poor scheduling performance in existing intelligent scheduling systems for production lines. The system includes a data acquisition module: analyzing production demand for target orders to be executed, obtaining the quantity of products in the target order, performing historical production quantitative analysis on the production line executing the order based on the quantity of products in the target order, and acquiring production line scheduling data based on the analysis results; a production line matching module: matching production lines for target orders to be executed, obtaining target-suitable production lines; a production analysis module: monitoring the production of sample production units included in the target-suitable production line, obtaining the overall efficiency of the unit equipment based on the monitoring results, and obtaining production scheduling analysis data; and a production scheduling module: performing real-time production scheduling for the product production units on the target-suitable production line. This invention can improve the scheduling efficiency of intelligent scheduling systems for production lines.

This invention discloses a big data-based method and system for managing the production of foam shoes, belonging to the field of production management technology. Specifically, it includes: establishing an order contamination feature database by collecting historical production data; obtaining the contamination propagation patterns of different colored raw materials during the production process and calculating the unit contamination coefficient of each colored raw material; constructing an equipment contamination state evolution model, establishing a mapping relationship table between equipment contamination state and pass rate, and predicting the pass rate distribution of each order by simulating changes in equipment contamination state under different production schedules; and establishing a multi-objective optimization function to comprehensively score candidate production schedules based on order delivery time requirements, selecting the optimal production schedule to drive the production system execution. This invention quantifies the relationship between color contamination and product quality, effectively ensuring stable product quality while significantly improving equipment utilization and production efficiency by optimizing production scheduling and reducing unnecessary cleaning.

The present application relates to a kind of steel pipe whole-process traceability and intelligent scheduling lean MES system, belong to steel pipe production technical field, including: unified identification and data acquisition module, for each steel billet / steel pipe is given unique identity code;Whole-process traceability management module, to unique identity code as index constructs "product digital twin archives";Intelligent scheduling and dynamic optimization module, for receiving production plan from ERP system generates process level scheduling instruction;Lean execution and visual monitoring module, for generating electronic job instruction according to process level scheduling instruction;System integration and closed-loop feedback module, for storing process level scheduling instruction and product quality in system knowledge base during production process, realize the continuous self-learning of system.The present application realizes material, process, quality data whole-process association and accurate traceability by constructing unified product identification system.

The invention discloses a production prediction method for a flexible manufacturing system. The production prediction method comprises the steps of 1, setting algorithm preconditions and scheduling strategy requirements, 2, constructing a core element model, and 3, executing production whole-process simulation prediction. The method effectively solves the problem of production prediction of a flexible manufacturing system caused by uncertainty of varieties, processes and cutters and influence of a scheduling strategy, can simulate the whole production processing process, outputs multi-dimensional results such as work order completion time, a blank and cutter preparation list, a production Gantt chart, a production trend chart and an equipment starting rate, and improves the production efficiency. And reliable data support is provided for a user to adjust a production plan and configure resources.

The present disclosure discloses a blockchain-driven automated 3D printing modular construction management system, comprising: a decentralized blockchain network deployed on node devices of one or more stakeholders, wherein the one or more stakeholders include one or more of a 3D printing assembly manufacturer, a quality inspection team, a regulatory body, a logistics provider, a consultant, a budget team, and an end-user / client / owner, wherein the decentralized blockchain network is configured to store immutable records of full-lifecycle data including design specifications, material procurement, 3D printing production, logistics transportation, on-site assembly, final handover, and post-construction maintenance; a 3D printing hardware module including at least one 3D printer integrated with blockchain nodes, wherein the 3D printing hardware module real-time records printing parameters, material usage, and quality inspection data to the decentralized blockchain network, and synchronizes digital files, production schedules, and quality control measures with the network; an IoT sensor module comprising a material parameter sensor, a production progress sensor, and a transportation tracking sensor, wherein the IoT sensor module transmits real-time data to the decentralized blockchain network after a computing preprocessing using optimized computational algorithms, and supports real-time verification of construction processes; and a smart contract module embedded in the decentralized blockchain network, wherein the smart contract module includes pre-defined terms for automated material authentication, supply chain transaction validation and milestone-based payment triggering, and links financial transactions directly to project milestones.

The embodiment of the invention discloses a dynamic updating method and system for industry production, and the method comprises the steps: generating standardized incremental knowledge data based on newly-added industry production data obtained in real time; updating a pre-established knowledge graph library in real time based on the standardized incremental knowledge data; and in response to an input demand, outputting a reasoning result based on a pre-constructed probabilistic reasoning engine and the updated knowledge graph library. According to the embodiment of the invention, the knowledge graph library on which production reasoning depends can be continuously updated, and the knowledge graph library is updated only by standardized incremental knowledge data without integrally replacing the content of the knowledge graph library, so that the agent updating efficiency is greatly improved, and the influence on the production schedule is avoided.

This invention relates to the fields of intelligent manufacturing and industrial internet technology, and discloses an intelligent scheduling method and system for the continuous production of automotive structural components across multiple processes. The method includes: acquiring equipment status data and material flow location data to obtain a current production state vector; obtaining a preliminary production schedule using a genetic algorithm based on the current production state vector; generating adjustment demand signals based on process time fluctuation indicators; obtaining an optimized production sequence using a simulated annealing algorithm based on the adjustment demand signals; embedding emergency order response demands based on production disturbance factors to obtain a final execution plan; loading the final execution plan into a preset discrete event simulation engine to generate virtual work trajectories, comparing and generating deviation correction parameters, and parsing to generate a production instruction sequence. This method can improve the dynamic adaptability and execution accuracy of the scheduling scheme.

A computer-implemented method of managing industrial power consumption is provided. The method comprises: receiving measured aggregate power consumption data indicating power consumption of an assembly of machines during a first time-period, wherein each machine of the assembly of machines is capable of operating in a plurality of different operating states; accessing production schedule information indicating the operating states of each machine of the assembly of machines during the first time-period; based on the aggregate power consumption data and the production schedule information, calculating disaggregated power consumption data for each machine of the assembly of machines during the first period, wherein the aggregate power consumption data and the disaggregated power consumption data comprise time series data indicating power consumption during the first time-period.

The invention provides a production scheduling method for a sheet workshop in the ship industry. The method is applied to the technical field of sheet workshop production scheduling in the ship industry and comprises the steps that workshop production scheduling requirements are received, segment information and processing resource parameters of ships to be subjected to production scheduling are acquired, and a production scheduling time window is determined; wherein the processing resource parameters comprise the number of stations, the equipment processing efficiency and the personnel configuration intensity; on the basis of the structural complexity, the delivery emergency degree and the machining process requirements of the ship segments to be scheduled, the machining priority coefficient of the lower piece body of each segment is calculated; disassembling the processing procedure chain of the sheet body, identifying a potential bottleneck procedure in the procedure chain, and calculating the load rate of the potential bottleneck procedure; and if the load rate is greater than a preset load threshold, determining that the potential bottleneck process is a core bottleneck process, performing process allocation on the sheet body based on the processing priority coefficient, and generating an initial production scheduling plan. Therefore, the production scheduling efficiency of a sheet workshop is improved.

The invention relates to the technical field of intelligent production lines, and discloses a multi-product adaptive automobile air conditioner flexible production line intelligent production system which comprises a physical perception and execution layer, a digital twinning and simulation verification layer and a cloud intelligent decision and scheduling layer. Wherein the physical perception and execution layer comprises a multi-product adaptation execution unit used for adapting to different types of automobile air conditioners on a single production line; the digital twinning and simulation verification layer is used for performing simulation verification and feasibility rehearsal on the production plan and the scheduling instruction; and the cloud intelligent decision-making and scheduling layer comprises a hybrid intelligent scheduling engine and a self-adaptive process knowledge base. According to the invention, through linkage of three-dimensional visual real-time identification and the adaptive process knowledge base, perception and adaptation are realized; and when the new-type product is on line, the whole set of digital process parameters are automatically identified and called to drive the production line to adjust, so that the whole-flow process switching time is greatly shortened, and the switching efficiency is improved.

The invention provides a production scheduling method, device and equipment, a storage medium and a program product, and relates to the technical field of intelligent manufacturing and workshop scheduling, and the method comprises the steps: obtaining the resource configuration data of a plurality of RMCs (Reconfigurable Manufacturing Units) and the operation process structure data of a to-be-scheduled operation; according to the resource configuration data and the operation process structure data, determining a state characteristic value input into the production scheduling agent, and obtaining a scheduling scheme of the to-be-scheduled operation output by the production scheduling agent according to the state characteristic value; the scheduling scheme comprises at least one of the following items: information of a selected process path, an execution sequence of process characteristics, an execution sequence of process operation and information of a selected RMC, the scheme of the invention can effectively cope with complex production and manufacturing scenes such as multi-operation, multi-process path, multi-RMC resource and processing capability switching, a global scheduling scheme is obtained, and the scheduling efficiency is improved. And the production scheduling efficiency and the system resource utilization rate are obviously improved.

A system and method for generating optimized workshop schedules is disclosed, involving a multi-step process that integrates data collection, optimization, and machine learning. The method begins by collecting production data and routing information from an Enterprise Resource Planning (ERP) system or an Enterprise Data Warehouse (EDW) system. An optimization model then generates an initial workshop production schedule based on the collected data. This schedule is subsequently fine-tuned to create an enriched schedule that incorporates alternative routing information and more realistic data constraints. A supervised Machine Learning (ML) model is trained using the enriched schedule to learn the embedded data constraints and objectives. Finally, the trained ML model is deployed to generate highly accurate workshop schedules, ensuring efficient and effective production processes. This method enhances scheduling accuracy and operational efficiency in manufacturing environments.

The present application relates to the technical field of data processing, and especially relates to a metal open-pit mine production scheduling optimization system based on multi-source data fusion, comprising: a data processing module, comprising a data acquisition unit for acquiring metal open-pit mine data of a plurality of data sources; a model training module, comprising a model generation unit for model training according to the feature extraction unit to generate a genetic algorithm model; a production scheduling module connected with the model training module, comprising an analysis unit connected with the model generation unit for analyzing the metal open-pit mine data through the genetic algorithm model to output an analysis result; and an optimization module connected with the data processing module, the model training module and the production scheduling module respectively, for determining time alignment tolerance of different data sources according to a loss rate of the metal open-pit mine data. The present application improves the optimization stability of metal open-pit mine production scheduling.

This invention discloses an iterative optimization scheduling method and system for ship section manufacturing based on artificial neural networks. The method involves establishing a ship section manufacturing workshop model based on its state characteristic values ​​and constraints; acquiring historical production plans from the workshop; enumerating production plans using different scheduling rules; establishing a scheduling experience feedback pool; constructing and training a backpropagation (BP) neural network for iterative optimization scheduling of ship section manufacturing; calculating the scheduling rule priority weight matrix using the BP neural network based on the workshop's state characteristic values ​​and optimization target weights; executing the scheduling rules according to the priority weight matrix and recording the scheduling results in the scheduling experience feedback pool; and randomly selecting small batches of experience samples to update the parameters of the BP neural network. This invention achieves good scheduling results and can improve production efficiency and resource utilization in the ship section manufacturing process.

The application belongs to the technical field of steel production and artificial intelligence, and relates to a construction method of a hot rolling scheduling hierarchical reinforcement learning model, comprising the following steps: obtaining a single product order data set according to hot rolling production daily plan order data, and constructing a hot rolling production scheduling sample data set based on the data set; presetting a daily rolling total number, constructing a hot rolling scheduling hierarchical reinforcement learning model corresponding to all rolling processes, training the model by using a training sample data set, and generating an overall rolling order scheduling sequence; and verifying the trained model by using a verification sample order data set to obtain a final hot rolling scheduling hierarchical reinforcement learning model; the application improves model decision efficiency and training stability, effectively adapts to order structure changes and process parameter updates, ensures that the generated scheduling scheme meets process feasibility and production stability requirements, and significantly reduces the calculation cost caused by invalid action exploration.