247 results about "Production schedule" patented technology

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent production scheduling method and system for a manufacturing system, and the method comprises the following steps: collecting an equipment state, a material neat rate and an order emergency degree in real time based on dynamic production data, and calculating an equipment availability coefficient, a material guarantee index and an order priority score through feature analysis; and forming a dynamic production feature set. The system constructs a production scheduling optimization model through work order-equipment matching degree calculation and process priority optimization, and performs multi-objective optimization solution by adopting an NSGA-III algorithm to realize the optimal combination of equipment utilization rate, order delivery rate and inventory balance. Meanwhile, an arbitration mechanism is introduced to coordinate process priority conflicts, and the scheme is verified through time, space and dynamic adaptability, so that feasibility and stability of production scheduling execution are ensured. According to the invention, the production scheduling efficiency of the manufacturing system can be improved, the equipment utilization rate can be improved, the order delivery delay rate can be reduced, and the adaptive ability to the change of the production environment can be enhanced.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses a dynamic production plan adjustment method in an industrial internet environment, and the method comprises the steps: constructing a task dependence graph according to the task attribute data of a production task; identifying a task state change event, and updating the task dependency graph in real time based on task nodes and edges influenced by the task state change event; a linear programming algorithm is adopted to obtain a task priority ranking result, and task priority ranking is dynamically adjusted; a distributed scheduling framework is adopted to map tasks to scheduling queues of all edge nodes, and a task distribution strategy is determined; and marking the abnormal task based on the task performance index in the task execution process, identifying the abnormal mode of the abnormal task, and optimizing the task allocation strategy based on the abnormal mode data of the abnormal task. The invention provides a dynamic, self-adaptive and efficient production plan adjustment method, which can comprehensively improve the global optimization capability, real-time performance and intelligent level of task scheduling in a dynamic production environment.

The invention discloses a production scheduling method and device based on digital twinning, equipment and a medium, and relates to the field of intelligent manufacturing, and the method comprises the steps: collecting production data, generating an initial production state, defining an initial production scheduling constraint condition, and constructing a digital twinning model; predicting the available capacity of the production resources through a digital twinborn model, identifying a risk period, and updating the twinborn state of the production resources; coupling the predicted production resource available capacity with the order set, generating a task priority queue and updating production scheduling constraint conditions; executing scheduling optimization and simulation detection conflicts in the digital twin model, if conflicts exist, executing local rearrangement based on conflict information until requirements are met, and generating an executable scheduling scheme; and issuing the executable scheduling scheme to a production and logistics link, monitoring execution deviation, rescheduling an affected task, completing a production task and updating the digital twin database. According to the invention, the real-time performance and accuracy of scheduling response are improved.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The invention relates to the technical field of intelligent scheduling, and discloses an intelligent scheduling method and system for a factory production plan, and the method comprises the steps: carrying out the three-dimensional scanning and sensor data collection of a factory environment, and obtaining a digital twin model and a real-time production data flow; according to the digital twinborn model and the real-time production data flow, performing prototype comparison learning processing on a double-tower encoder to obtain a pre-training scheduling model; performing knowledge constraint and knowledge distillation processing on the pre-trained scheduling model to obtain a knowledge enhanced scheduling model; according to the knowledge enhanced scheduling model, constructing a double-loop coordinated scheduling system comprising a production intelligent control loop and a resource dynamic allocation loop; and performing multi-objective optimization and predictive scheduling calculation based on the double-loop coordinated scheduling system to obtain a production plan scheduling scheme. Therefore, the problem of knowledge migration of a traditional scheduling system when production conditions change is effectively solved, and the scheduling accuracy in a dynamic production environment is improved.

The invention relates to the technical field of big data, in particular to a real-time commodity sales prediction system and method based on edge calculation. The system comprises a data acquisition module, an edge calculation module, a commodity sales prediction module, an optimization feedback module and an intelligent scheduling module. The system collects sales, inventory, customer behaviors and environment data in real time through the Internet of Things, and utilizes a graph embedding technology to construct a dynamic relation graph and enhance features. Modeling is carried out based on a space-time diagram network algorithm, and accurate prediction of commodity sales volume is realized. Model parameters are optimized through reinforcement learning, inventory replenishment, a distribution path, a production schedule and a promotion strategy are dynamically adjusted in combination with a discrete differential evolution algorithm, and a real-time resource scheduling scheme is generated. According to the invention, the accuracy and real-time performance of commodity sales volume prediction are improved, optimal configuration of resources is realized through intelligent scheduling, and the operation efficiency and market competitiveness of enterprises are effectively improved.

The invention relates to the technical field of production scheduling, in particular to an optimal scheduling method for hardware mold production. The method comprises the following steps: collecting hardware mold parameters and carrying out topological structure analysis to generate a hardware topological structure and a skeleton, then simulating a skeleton assembly process and recording a movement track, carrying out time sequence arrangement on the track by using mold production historical data, identifying a conflict track and a coordination track, and finally, carrying out hardware mold assembly. Resource requirements are calculated according to mold parameters, parallel production scheduling is carried out, meanwhile, the dependency relation between the steps, conflict track rearrangement steps, coordination and conflict rearrangement production plans are deduced, scheduling optimization is carried out, and intelligent scheduling of hardware mold production is achieved. The optimal scheduling method for hardware mold production is more efficient.

The invention discloses an AI-based automatic production line scheduling system in an industrial internet, which relates to the technical field of production scheduling and comprises a production plan management module S1, a dynamic scheduling engine module S2, a resource scheduling module S3, a real-time monitoring system module S4, an exception handling center module S5 and a data optimization platform module S6. In the industrial internet, an AI-based automatic production line scheduling system, an X dynamic scheduling engine millisecond response and a multi-agent reinforcement learning engine based on a federated learning architecture realize millisecond response scheduling, each device is used as an autonomous decision-making unit, and dynamic coordination is performed through a distributed Q learning algorithm, so that the vacancy rate of the devices is greatly reduced, and the scheduling efficiency is improved. According to a long-short-term memory network deep analysis model of order delivery cycle compression, emergency order insertion response speed improvement, multi-modal AI quality monitoring, fusion of vibration, thermal imaging and current spectrum, the detection rate is greatly improved compared with a unified sensor, causal reasoning and root cause analysis are performed, a fault causal graph is constructed to position a deep problem, and the average repair time is shortened.

The invention relates to the field of flexible workshop production scheduling, in particular to a flexible workshop production scheduling method based on multi-agent collaboration, and the method comprises the steps: obtaining a production scheduling demand of a production manager through a main agent in a natural language form, and carrying out the intention recognition of the production demand through an intention-related agent; a tool is called through an information agent to collect production information, then a scheduling negotiation stage is started, and a main agent and a scheduling agent carry out multi-round negotiation; in each round of negotiation, the main agent outputs a production plan according to the result of the intention recognition stage, and the scheduling agent calls a scheduling tool to generate a scheduling scheme according to the production plan of the main agent; and if the generated production plan or scheduling scheme does not meet the requirement, entering the next round of negotiation. According to the invention, production scheduling requirements of production managers can be continuously obtained in a natural language form, and production plans and scheduling schemes with feasible resources and time can be timely given.

The invention relates to the technical field of production management in the injection molding industry, and discloses a method and system for automatically generating a production scheduling plan based on production in the injection molding industry, and the method comprises the steps: obtaining a production order, an equipment state and material inventory data, generating original production parameter data, and deploying a distributed production scheduling network; collecting and analyzing multi-stage equipment load data and equipment state data, and generating corresponding influence factors; order priority data are collected, and production task areas are divided; and optimizing and calculating a production scheduling plan based on the task area data, integrating the multi-factor data to generate comprehensive production scheduling data, and further generating and outputting a production scheduling instruction. The system comprises a production scheduling network deployment module, an equipment load analysis module, an equipment state analysis module, a production task area analysis module and a production scheduling instruction generation module. Through multi-data integration and scientific modeling, the intelligent precision of injection molding production scheduling is realized, the equipment management efficiency, the order delivery capability and the overall production efficiency are improved, and the system is suitable for automatic production scheduling management in the injection molding industry.

The invention belongs to the technical field of intelligent manufacturing, and discloses a production order automatic scheduling correction method and system, and the method comprises the steps: obtaining a product material list; determining a product process route according to the product type and the process comparison result; marking resource configuration of a process route and a production scheduling state of the product; creating a production order for the products of which the production scheduling states are to be scheduled, and forming a production order level material list according to order information of each level and a product material list; according to the production requirements of the products, the production scheduling logic of the products is selected from the shortest construction period logic, the earliest delivery time logic and the emergency order insertion logic, and production scheduling of the production work orders is carried out; when the production work order is arranged, generating a processing equipment Gantt chart based on the processing information of the processing equipment and the production work order information; and correcting the production scheduling plan of the production order by using the automatic correction logic in the Gantt chart of the processing equipment. The automatic production scheduling efficiency of production is improved, and the customer satisfaction is reduced and improved.

The invention discloses an intelligent production management order arrangement system, which belongs to the technical field of production management, and comprises an order management module, an intelligent production arrangement module, a resource integration and optimization module, a real-time monitoring and adjusting module and a data analysis and decision support module, through setting an order management module, an intelligent production scheduling module and a resource integration and optimization module, the order management module manages and grades orders during use, and the intelligent production scheduling module produces an optimal production schedule according to the order and equipment information and an algorithm. The resource integration and optimization module intelligently distributes resources by means of detailed modeling and analysis of resources such as equipment information and manpower, maximum utilization of the resources is achieved, the efficiency of production management scheduling is greatly improved, and the requirements of industry production can be met according to different equipment information.

The invention provides a steel cutting and production scheduling method and device based on dynamic risk assessment, and relates to the field of steel processing pre-production management. Order, equipment and inventory data are collected in real time, and a multi-dimensional numerical matrix is constructed to achieve data structured expression; adopting a Monte Carlo method to generate a candidate production scheduling sequence, combining leftover material loss and delay risk dynamic evaluation, and fusing simulated annealing and an adaptive threshold mechanism to carry out iterative optimization; and through probability similarity screening and regression fitting, an optimal production scheduling scheme is determined in an error allowable range. The method effectively balances the production efficiency and the resource utilization rate, reduces the cutting loss and the delivery risk, and is suitable for the intelligent production scheduling decision of a complex manufacturing scene.

The invention relates to the technical field of ship manufacturing intelligent management, in particular to a ship pipeline design drawing stage management method and a ship pipeline design drawing stage management system. Production schedule and process specification data are acquired and processed in a structured mode, a standardized input set is constructed, conflict resolution and progress and cost optimization are carried out by utilizing a multi-objective optimization engine; a four-dimensional model, semantic verification and dynamic scheduling are combined to generate a preliminary pipeline scheme, construction drawings and animations are output after three-dimensional simulation verification, feedback is collected in real time in construction, secondary scheduling and optimization are triggered, continuous iteration and accurate execution of design are achieved, and through intelligent decision making of a multi-target optimization engine, the design drawing efficiency can be improved by 40% or above. Meanwhile, the design error rate is reduced by 90%, and later reworking is greatly reduced.

The invention discloses an intelligent production scheduling management system for a hardware mold, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of slow scheduling reconstruction under disturbance. According to the method, rapid response and local adjustment of resource disturbance events such as equipment faults and personnel absence are realized by constructing a disturbance identification and scheduling reconstruction mechanism, the system acquires equipment utilization and scheduling information, generates a joint time sequence, calculates a disturbance sensitivity index and constructs a task resource influence map, and the task resource influence map is subjected to resource scheduling. Positioning a disturbed task and a critical path; a scheduling adjustment strategy in a minimum disturbance range is generated in combination with task priority factors, equipment load balance coefficients and scheduling cost, scheduling confusion caused by global rearrangement is avoided, a system is linked with mold allocation and task queue control, a closed-loop scheduling execution process is formed, and the scheduling efficiency is improved. And the production scheduling stability, the self-adaptive capability and the task completion efficiency can be improved in a complex manufacturing scene.

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 technical field of cloud edge collaboration, in particular to a production scheduling intelligent management and control system based on cloud edge collaboration, and the system comprises a task feature aggregation module which is used for extracting a task type code based on an input task request, querying a task type and calculation amount mapping table in a pre-stored knowledge base, matching the task type and extracting a calculation amount parameter. According to the method, the mapping relation between the task type and the pre-stored calculation amount is dynamically matched, the task data packet size, the real-time requirement threshold value and the bandwidth reference value are linked to calculate the transmission time consumption parameter, quantitative mapping of task requirements and resource supply is achieved, and 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, and the multi-dimensional node state vector is generated in combination with the number of cloud virtual machine instances and memory surplus data, so that the limitation of traditional single resource index monitoring is broken through, and the space-time fine granularity of load evaluation is improved.

The invention relates to the technical field of intelligent manufacturing, particularly provides an AGV task allocation method, electronic equipment and a storage medium, and aims to solve the problems of low AGV starting rate and low production efficiency caused by the fact that an existing AGV scheduling system rarely pays attention to optimization of a dynamic task allocation algorithm. In order to achieve the purpose, the method comprises the steps that operation stages, including a first stage, a second stage and a third stage, of a current AGV task allocation scene are obtained; determining corresponding task allocation strategies based on the operation stage, wherein the task allocation strategies comprise an event-driven allocation strategy, a Hungary algorithm allocation strategy and a heuristic algorithm allocation strategy; and carrying out AGV task allocation based on the determined task allocation strategy. Through the above implementation mode, a coupling scheme of multiple task allocation algorithms is adopted, different task allocation strategies can be adopted in different operation stages, the requirement for dynamic adjustment of production tasks is met, the tasks can be reasonably formulated according to the production plan, and the AGV starting rate and the production efficiency are improved.

The invention provides a production scheduling method, device and equipment. The method comprises the following steps: acquiring real-time production data through an intelligent manufacturing system; determining constraint conditions of a target optimization model according to the real-time production data; obtaining a plurality of dimension production targets; determining a composite objective function according to the multi-dimensional production objective; determining a target optimization model according to the constraint condition and the composite target function; iteratively solving the target optimization model through at least one optimization model to obtain a target solution set; and controlling a production schedule module to output a target production schedule according to the target solution set. According to the invention, the scheduling quality and efficiency of the production line are improved, and the adaptability to the dynamic production environment is enhanced.

The invention discloses an enterprise electric power energy monitoring analysis method and system based on the industrial internet, relates to the technical field of energy monitoring, and provides reliable data for a system by collecting operation electric power data and production management system data through a sensor, preprocessing the data and storing the data in a historical data set Dhis. And constructing an energy consumption prediction model through the historical data set Dhis to provide data support for a response strategy. And constructing a reward and punishment function based on the energy consumption prediction model, comparing the reward and punishment function with a reward and punishment threshold Rth, generating a power supply mode decision Mpow, and intelligently adjusting the power supply mode. And outputting the optimized production schedule Sopt by taking the total energy consumption cost TEC and the schedule stability SSP as optimization targets, so as to improve the production continuity. Model parameters are dynamically corrected through comparison of an optimization effect evaluation value Oscore and a threshold value Oth, it is ensured that the system continuously approaches the actual working condition in long-term operation, and strategy failure caused by equipment aging or process change is avoided.

The invention discloses a supply chain logistics real-time scheduling system and method, and relates to the technical field of production scheduling, and the system specifically comprises a production plan dynamic adjustment module, a digital twin simulation module, a dynamic response and optimization module, a space-time coupling resource scheduling model, a cross-enterprise cooperation module, and a visualization and decision support module. PLTI instructions are dynamically generated through digital twinborn simulation and a dynamic response mechanism, in-plant and out-plant real-time collaboration and in-plant and out-plant combined modeling are achieved, the inventory turnover rate is increased by 25%, the inventory turnover rate is increased through a space-time coupling resource scheduling model, an in-plant storage model and an Amap API road network are constructed through a Unity 3D engine, instruction conflict probability is rehearsed, and the real-time performance of the system is improved. According to the method, logistics scheduling information is dynamically updated every 15 minutes based on a production plan, the logistics scheduling conflict probability is reduced by 83% through digital twin rehearsal, and real-time collaboration and intelligent scheduling of logistics inside and outside a factory are achieved through digital twin simulation, a dynamic response mechanism and a space-time coupling resource scheduling model.

The invention discloses a large model production and operation management system and method of an ERP system, and relates to the technical field of operation management. A production module collects processing data and dynamic data and synchronizes the data to the ERP system in real time; the financial module judges the matching between a budget and a plan by using the processing data and the dynamic data provided by the production module, and identifies problems in resource allocation; meanwhile, combining a random forest model to predict a cost fluctuation trend and a linear regression model to analyze the relevance between production efficiency and cost, and generating an optimized resource allocation and production scheduling scheme; the scheme is applied to a financial module and a production module, the financial module optimizes purchase budget and capital flow arrangement according to the adjusted resource allocation scheme, the production module readjusts task priority and a material use plan based on the optimized scheduling scheme, and all adjustment results are fed back to an ERP system in real time to form closed-loop management. And the production efficiency and the resource utilization rate are synchronously improved.

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 scheduling, in particular to an artificial intelligence-driven intelligent manufacturing system scheduling method, which comprises the following steps of monitoring order state parameters of a manufacturing execution system in real time, identifying equipment failure shutdown signals or order insertion signals, extracting associated equipment codes and current work order numbers, and establishing core disturbance information pairs. According to the method, the order state parameters of the manufacturing execution system are monitored in real time, the equipment fault shutdown signal and the order insertion signal are dynamically identified, and the equipment code and the current work order number are extracted, so that the core disturbance information pair highly associated with the disturbance source can be constructed, and the pertinence and accuracy of the scheduling process response are ensured. And in combination with the original plan completion time of the affected process, evaluating local delay caused by disturbance, gradually deducing to form the delay amount of the whole plan, describing the diffusion trend of the disturbance in the production progress, and defining a scheduling adjustment time window.

The invention relates to the technical field of big data analysis, in particular to a deep concentration production scheduling big data analysis system and method, and the system comprises a data collection module which collects concentration production real-time data, including temperature, speed and pressure information, and generates a real-time production data set; according to the method, multi-dimensional data such as temperature, speed and pressure in the production process are collected in real time, key performance indexes are accurately extracted, fine-grained capture and analysis of production information are achieved, production performance snapshots are formed, deep time sequence analysis is carried out on the basis, and the production trend and seasonal fluctuation are identified and predicted; the precision and reliability of trend prediction are improved; meanwhile, in combination with trend and seasonal analysis results, refined quality fluctuation monitoring is carried out on the production process, the fluctuation range of abnormal quality is found and positioned in time, and early warning and efficient management and control of production quality risks are achieved.

The invention discloses a vehicle manufacturing supply chain adaptive optimization method and system based on data driving, and relates to the related field of supply chain management, and the method comprises the steps: calling a historical manufacturing complete cycle data set, and obtaining a real-time manufacturing data set; carrying out integrated learning, carrying out association analysis on supply chain nodes according to a learning result, and constructing an inter-chain dynamic interactive simulation network; defining a manufacturing constraint condition, activating an inter-chain dynamic interactive simulation network to carry out demand prediction, and carrying out production analysis according to a prediction result; performing simulation execution of the production scheduling strategy to perform multi-objective optimization, generating a to-be-optimized label, performing dynamic monitoring, and updating the production scheduling strategy; and synchronizing the production scheduling global optimization strategy to a vehicle manufacturing supply chain for verification, and performing adaptive optimization according to a verification result. The problems that supply chain management lacks real-time dynamics, cannot respond to changes in time and is inaccurate in scheduling are solved, and the effects of improving real-time dynamics, responding to changes in time and improving scheduling accuracy are achieved.

The invention relates to an intelligent construction management method for an intelligent beam field, and the method comprises the steps: collecting multi-source data of a field beam field control system in a preset target period, and carrying out the preprocessing of the multi-source data into standardized input data; substituting the standardized input data into a productivity prediction model trained according to historical multi-source data, and generating a corresponding productivity interval; on the basis of a constrained optimization algorithm, generating a corresponding production scheduling result according to a pre-acquired production scheduling plan and a production capacity interval, and driving a field beam field control system to execute corresponding scheduling operation according to the production scheduling result; and when interference factors are detected, a new production scheduling result is dynamically adjusted based on the productivity prediction model, so that beam manufacturing, beam storage and beam discharging form a closed-loop collaborative management chain, thereby improving the precision and flexibility of a production plan, reducing resource waste and construction conflicts, and improving the overall operation efficiency and scheduling response capability.