71 results about "Work in process" patented technology

The invention discloses a production scheduling method and system of fast board orders, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring processing parameters and a processing sequence of a plurality of processes in a fast board order and production data of processing equipment corresponding to each process; based on the processing sequence and the completed number corresponding to each process, calculating the difference value of the completed number between the plurality of adjacent processes; according to the difference value of the number of finished products, the number of products in process between the processes is determined, and the buffer waiting time between the processes is set based on the number of the products in process; and calculating the machining duration deviation ratio between the workpiece machining time of each process and the corresponding planned machining time, and when the to-be-adjusted process with the machining duration deviation ratio exceeding a deviation ratio threshold exists, delaying the machining starting time of the subsequent process based on the buffer waiting time of the to-be-adjusted process. By implementing the technical scheme provided by the invention, the overall production efficiency of the fast order can be improved.

The invention relates to a production line vehicle type configuration transmission and verification method, and the method comprises the steps: obtaining vehicle type configuration data corresponding to a work-in-process when an intelligent transportation trolley enters an initial station of a production line; the vehicle model configuration data are written into the flexible conveying bracket provided with the work-in-process product; the intelligent transportation trolley is controlled to carry the flexible conveying bracket to enter a plurality of target stations matched with the vehicle type configuration data; when the intelligent transportation trolley arrives at any target station, vehicle type configuration data are read from the flexible transportation bracket; verifying the configuration state of the current station based on the vehicle type configuration data; if verification is not passed, the target station is controlled to execute configuration switching operation, and verification is executed again after configuration switching is completed; and if the verification is passed, controlling the target station to execute the corresponding production operation. By adopting the method and the device, the consistency of the vehicle type configuration data acquired by the station and the actual vehicle type configuration data of the product in process can be improved, and the processing abnormity caused by wrong station configuration is reduced.

The invention discloses a vehicle door handle accessory manufacturing management system for an electric vehicle, particularly relates to the field of intelligent manufacturing, and is used for solving the problems of production chain stability and efficiency caused by order insertion task disturbance. Production chain coupling constraint is converted into a quantum optimization model for global solution, and process collapse caused by traditional priority coverage is avoided. The anti-disturbance capability is evaluated based on process topological characteristics, local optimization of fragile links is locked, and the calculation overhead of whole-chain reconstruction is reduced. Digital twinning real-time verification and calibration ensure equipment load, material supply and process stability and balance, and reduce the risks of product overstock and equipment idling. By means of time window elastic adjustment, integration of an order insertion task and steady-state scheduling is achieved, continuity of production takt is maintained, emergency order response speed is improved, toughness of a manufacturing system is enhanced, manual intervention and trial and error cost are reduced, and reliable delivery and resource efficiency of multi-variety orders are guaranteed.

The invention discloses a flexible manufacturing resource collaborative allocation optimization method and system oriented to variable delivery periods, and relates to the technical field of scheduling control, and the method comprises the steps: installing a process completion confirmation label in each work-in-process link, and directly obtaining an equipment load, a work-in-process state and the completion time; in combination with order arrival prediction and equipment health conditions, a disturbance intensity evaluation algorithm is set, the current scheduling disturbance quantity is calculated, and the disturbance intensity of each sub-problem is quantified; based on the disturbance intensity of each sub-problem and the longest response time of the system, setting an adaptive sub-problem time window algorithm, and dynamically adjusting the time window range of the to-be-rearranged task; and carrying out hierarchical division on the key tasks and the secondary tasks in the sub-problem range, carrying out parallel calculation and then carrying out merging scheduling to form an updated collaborative scheduling scheme. According to the invention, during order change, equipment failure or material shortage, the affected subset tasks can be quickly identified, the scheduling range is limited, global recalculation is avoided, and quick response is realized.

The present application relates to the technical field of intelligent manufacturing and production scheduling, and particularly relates to a garment multi-process digital twin monitoring method, which comprises the following steps: establishing a process digital twin by using a colored time-delay Petri network and an event dictionary; hierarchically updating a processing time distribution and a line change cost matrix online; obtaining a coupling matrix and calculating a dual price vector by using a regularized optimal transport, and quantifying a work-in-process capacity upper limit as a work-in-process quota; calling a Koopman operator driven counterfactual generator to evaluate an incremental cost and generate a release sequence by optimal sorting within a rolling prediction window; locking and issuing a process path according to the quota, implementing a reversible fine-tuning based on a deviation, and triggering a withdrawal and path diversion according to a topological structure abstract. The present application reduces work-in-process peak value and line change loss, and improves production capacity utilization and stability of on-time delivery.

The invention discloses a locomotive maintenance layout optimization method, which comprises the following steps of: after determining a main line node beat, reversely deducing and calculating the quantity of products in process of parts by utilizing part demand time of each node, and determining the storage area of the parts; secondly, the components with the same route are split and packaged by means of process route analysis of the components, and a modular functional layout is formed; and finally, carrying out logistics route optimization by using a path analysis method, adjusting the layout of the functional modules, and finally forming the overall locomotive maintenance layout. The problems of inflexible layout, long logistics path, low site utilization rate and the like in the prior art are solved.

The application discloses an assembly type workshop roller scheduling method and belongs to the field of workshop scheduling. In view of the low scheduling efficiency and poor accuracy of the prior art, the application provides an assembly type workshop roller scheduling method, which comprises the following steps: after a current roller detects a work-in-process product, it is judged whether the next section of the roller is full of the work-in-process product; if not, the work-in-process product of the current roller is transported to the next section of the roller; if yes, a second judgment is performed, and different scheduling modes are adopted according to the result of the second judgment. The application adopts a double judgment mode to determine whether the next section of the roller reaches a threshold value, which not only ensures that the work-in-process product passes through the roller in an orderly manner, but also solves the problem that the system cannot be scheduled due to the misstorage of the roller caused by the failure of photoelectric and other detection devices, maximally guarantees the stable operation of the system, and enables the work-in-process product to be more safely and efficiently transported to the destination; and the overall method is simple to operate, high in work efficiency and safety.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to an intelligent order scheduling and material management system for a milling and turning workshop. It includes a task parsing module, a feature mapping module, a state acquisition module, a potential field construction module, a scheduling calculation module, and a feedback execution module. The system receives task drawing data and bill of materials data, extracts geometric feature bounding dimension data and machining tool vector sequence data, and tensors and encodes them. It collects the current tool library configuration status parameters of the target machining equipment and the physical attribute parameters of work-in-process, constructs a continuous potential energy field distribution matrix, and dynamically refreshes it. It calculates the potential energy gradient descent direction of the multidimensional machining feature tensor flow, generates a task queue of associated paths and a theoretically estimated execution cycle time sequence, issues CNC machining task plans and automated guided vehicle (AGV) allocation instructions, and feeds back the actual cycle deviation correction value for closed-loop updates. This invention makes the scheduling results closer to the actual executable state of the workshop.

The invention relates to the technical field of intelligent manufacturing and production scheduling, in particular to a clothing multi-process digital twinning monitoring method, which comprises the following steps of: establishing process digital twinning by using a colored time delay Petri network and an event dictionary; the processing time distribution and the line changing cost matrix are updated through layered online assimilation; a coupling matrix is obtained through regularization optimal transmission, a dual price vector is calculated, and the upper limit of the capacity of the work-in-process is quantified into the quota of the work-in-process; an anti-fact generator driven by a Koopman operator is called in the rolling prediction window to evaluate increment cost and preferentially sort to generate a release sequence; and locking and issuing the process path according to the quota, implementing reversible fine adjustment based on the deviation, and triggering withdrawal and path change according to the topological structure abstract. The product peak value and the line change loss are reduced, and the stability of capacity utilization and on-time delivery is improved.

The invention discloses a production process quality system risk assessment method for a digital workshop, and relates to the technical field of intelligent quality management and control, and the method comprises the steps: endowing each work-in-process entering the digital workshop with a unique initial risk identifier; after the processing of the current procedure is completed, collecting industrial data of the in-process product, generating a risk characteristic value based on the industrial data, and updating the initial risk identifier to form a dynamic risk chromosome; the industrial data comprises quality detection data, process parameter flow and equipment state data; constructing a cellular automaton network based on a process logic relationship in the production scheduling plan; when the work-in-process flows to a new process, inputting the risk characteristic value into a cellular automaton network, and calculating a risk conduction direction and a risk conduction probability through a cellular state evolution equation; according to the method, the process logic relation in the production scheduling plan is mapped into the cellular automaton network, so that dynamic modeling and quantitative analysis of the risk conduction path in the production process are realized.

The invention discloses a shoe production takt intelligent scheduling and control method based on MES data driving. The core of the method is that the queue length, waiting time and workpiece color matching information of each process node are collected in real time; dynamically calculating a process node comprehensive pressure value fused with the color mismatch degree; identifying the maximum pressure node of the whole line; a scheduling instruction is generated according to the color mismatch degree and comprises the steps of commanding upstream throttling and downstream dredging and triggering a priority pairing instruction when the color mismatch degree exceeds the standard; visual guide execution is carried out through a station self-adaptive metronome; and meanwhile, the feeding rhythm is globally regulated and controlled based on tail end pressure The production line is regarded as a dynamic pressure system, conversion from static planning to dynamic data driving is achieved, dynamic bottlenecks are effectively eliminated, inventory of products in process and'isolated shoes' is greatly reduced, and production efficiency and order delivery punctuality rate are remarkably improved.

The invention discloses a digital twin-driven LCD backlight plate production collaborative management and control method and system, and particularly relates to the technical field of production management. The method is used for solving the problems of resource mismatching, quality risk and efficiency loss caused by neglecting the capability difference of a production line in a specific process and a dynamic coupling relationship between process paths of products in use in an existing static planning method. A digital twin model corresponding to a physical production line is constructed, a process capability vector and a process path vector are integrated, a candidate production line set is identified based on vector data, a parameter resonance risk level is evaluated, a key bottleneck process in a process network topology is positioned, and a dynamic routing scheme is generated to avoid high-risk paths and bottlenecks. And a manufacturing execution system triggers real-time adjustment of a product production path to realize dynamic collaborative optimization of production resources and order demands.

The present application belongs to the field of shaft gear heat machining, and particularly relates to a long shaft gear heat front automatic production process and a line body thereof. The process comprises: a truss mechanical hand grabbing a workpiece after lathe machining; the truss mechanical hand conveying the workpiece to a marking station and completing marking; a six-axis robot grabbing the workpiece to a cleaning mechanism for cleaning; the six-axis robot placing the cleaned workpiece on a detection station for detecting an inner hole; after detection, the six-axis robot feeding the workpiece to a broaching machine for machining; the six-axis robot grabbing the machined workpiece and placing it into a discharging bin; the production process and the line body realize machining of long shaft semi-shaft gears from precision forgings to finished products before heat, and realize automation in the whole process, reduce site occupation, turnover times, work-in-process inventory, etc., and realize an efficient, stable, multi-species modern automatic production line.

The invention discloses a noise online detection device and method for a driving unit assembly, and belongs to the field of noise detection. The device comprises a control system, a supporting structure, an electric appliance cabinet, a soundproof box, a detection probe, a lifting plate, a fixing assembly, a load assembly, a driving assembly and the like. Wherein the lower box body can form a closed environment on the outer side of a detection station, so that the noise influence in a workshop environment is isolated, a streamlined assembly line directly penetrates through the online detection device, and a driving unit assembly needing to be detected can be lifted to be separated from a conveying line through a lifting plate until the driving unit assembly reaches the detection station for subsequent detection work. The whole process is high in automation degree, online detection can be completed without transfer of the driving unit assembly, the whole production period can be effectively shortened, the production efficiency is improved, and a large number of intermediate products do not exist.

The invention relates to a production line tool file automatic monitoring method and system. According to the technical scheme, the method comprises the steps that a production list of material numbers of products and file lists under all process production file storage paths are obtained regularly; obtaining a production process flow list corresponding to the material number of each product in process according to the production list; generating a demand file list corresponding to each product number according to the production process flow list corresponding to each product number; according to any demand file list and the file list, judging whether the files in the file list accord with the corresponding demand files in the demand file list, and if not, generating feedback information of lack of the corresponding demand files; displaying and exporting the feedback information; according to the method and the device, automatic monitoring of tool file supply is realized, production delay caused by tool file missing is avoided, and the production efficiency is improved.

The application discloses a kind of industry chain quality analysis methods based on big data, it is related to artificial intelligence technical field, the method is in the process of multi-vehicle mixed line production in automobile industry chain, based on order release cycle time, logistics arrival interval time and station actual operation cycle time Construction rhythm alignment dataset, and utilize cross correlation analysis to calculate rhythm phase shift Pm, combined with line edge work-in-process residence time and station beat adjustment frequency Construction rhythm disharmony effectiveness determination condition set, to convert the time mismatch relationship between upstream supply rhythm and downstream assembly rhythm into calculable, comparable quantitative index.Compared with prior art which only relies on result quality indicators such as pass rate and rework rate, the present application can identify the rhythm disharmony state of the industry chain in advance before the quality problem is explicit, significantly improving the foresight and sensitivity of the industry chain quality risk identification.

A multi-variety processing group line opportunity maintenance system considering quality reliability coupling comprises a data acquisition and degradation parameter estimation module, a comprehensive cost evaluation module, a baseline preventive maintenance plan generation module and a fault detection and opportunity maintenance decision module. According to the method, the comprehensive cost in multiple aspects of the production process including the product quality is quantified, a preliminary preventive maintenance (PM) plan is firstly generated on the basis of the production schedule, and then the optimal opportunity maintenance strategy is obtained by taking the total cost in an opportunity window as a target function under the sudden fault situation. And analyzing a system performance evolution rule before and after maintenance, thereby remarkably improving the accuracy and economy of a maintenance decision.

This invention discloses a real-time monitoring and optimization system for intelligent factory production processes, belonging to the field of intelligent factory production management. Specifically, it includes: acquiring process parameters and production action sequence data of the pipe fitting forming process using the Industrial Internet of Things (IIoT) and transmitting them to a data processing center; constructing a motion waste analysis model based on Markov chain theory and the motion sequence data; identifying motion waste by calculating state transition probabilities and steady-state distributions and comparing them with standard motion flows; and feeding the results back to the production management system; combining process parameters and motion waste identification results, the production management system uses queuing theory and operations research to construct a dynamic bottleneck station identification model, calculating the probability of each station becoming a bottleneck in real time, and determining the dynamic bottleneck station based on the amount of work-in-process inventory and production efficiency; and adjusting the production cycle based on the dynamic bottleneck station identification results to achieve real-time monitoring and optimization of the production process, thereby improving production efficiency and quality.

The application discloses a hanging warehouse scheduling method and device, equipment and medium, and relates to the technical field of hanging warehouses. When a hanging warehouse in-process product shelving device needs to store in-process products, the in-process product shelving device selects a plurality of available shelving locations from a warehouse area according to storage selection rules, and sorts the available shelving locations according to storage sorting rules to store the in-process products in the optimal shelving location in the sorted shelving locations. When a hanging warehouse in-process product processing device needs to dispatch in-process products from the warehouse area, the in-process product processing device selects a plurality of available dispatch locations from the warehouse area according to dispatch selection rules, and sorts the in-process products in the available dispatch locations according to dispatch sorting rules to dispatch the target out-of-warehouse product in the sorted products to the product processing device, and dispatches the products in the warehouse area before the target out-of-warehouse product and reflows the products to the shelving device. The application combines the use of storage and dispatch selection rules and storage and dispatch sorting rules to effectively schedule the storage and dispatch of products between different devices, and improves the product scheduling efficiency.

The integrated flexible manufacturing system of production workshop line library and the manufacturing method of electric heating tubes belong to the technical field of flexible manufacturing. It is based on the integrated flexible manufacturing system software system, AGV control system, vertical library control system and enterprise resource management system; it includes a group of production lines, in-process shuttle car storage system and AGV cars, and each production line is configured with a corresponding production line side material calling device. In the present invention, through the connection of the integrated flexible manufacturing system software system, a logistics connection is established between the in-process shuttle car storage system and various production lines through AGV; the manufacturing process is completed by intelligent logistics equipment, realizing unmanned operation of the whole process; at the same time, for the in-process storage shuttle car storage system, refined management is realized, the in-process products can be monitored in real time, and it can also assist in formulating a reasonable production plan and balancing production.

This invention discloses a digital twin-driven collaborative management and control method and system for LCD backlight panel production, specifically relating to the field of production management technology. It addresses the resource misallocation, quality risks, and efficiency losses caused by existing static planning methods that neglect the dynamic coupling relationship between the capacity differences of production lines at specific processes and the process paths of work-in-process. The method involves constructing a digital twin model corresponding to the physical production line, integrating process capability vectors and process path vectors, identifying candidate production line sets based on vector data, assessing parameter resonance risk levels, locating key bottleneck processes in the process network topology, generating dynamic routing schemes to avoid high-risk paths and bottlenecks, and triggering real-time adjustments to the work-in-process production path through the manufacturing execution system to achieve dynamic collaborative optimization of production resources and order requirements.

The present application provides a management system and method for equipment for manufacturing battery components, relating to the field of automation. The system includes: an equipment PLC device, a data acquisition system, an EAP system, an equipment management system, a production management system, a flower basket traceability management system, and a KSR management system. The present application can replace manual labor to collect, monitor, and analyze production output and work-in-progress (flower basket) information in real time, with higher accuracy and real-time performance, and also reduces labor costs. By monitoring the production output and work-in-progress information on the production line in real time, on-site personnel can analyze the bottleneck links in the production process, thereby optimizing the production process and avoiding the accumulation of work-in-progress caused by untimely preparation, thus improving the overall production efficiency. By recording information such as the machines passed by and the in-and-out times of work-in-progress during the transfer process, and understanding the residence time of work-in-progress at each machine, waste phenomena in the production process, such as excessive waiting time and equipment idleness, can be discovered, thereby improving the equipment availability rate.

The invention provides a workshop control system model based on a double-layer control loop. According to the model, control variables are constructed in a flow rate form, and the order input rate and the productivity are selected as the control variables of the system, that is, the release of orders and the production capacity of the system are continuously adjustable. For a certain manufacturing unit, the constraint of the manufacturing unit is mainly from two aspects. On one hand, the manufacturing units are influenced by internal factors of the manufacturing units and need to be adjusted according to real-time states of the manufacturing units; and on the other hand, the manufacturing units are globally controlled by the workshop system. When the two are combined to see the problem, a local-to-overall framework is formed. According to the workshop control system model based on the double-layer control loop, a double-feedback control structure is designed from the angle of a control theory. And the production capacity adjustment response rate and the stability of the inventory level of products in process are improved.

Provided is a data processing method for processing data related to the production and management of a power storage element, wherein: with respect to different production lines in which the same type of raw materials or in-process items are used, different determination criteria for the same type of raw materials or in-process items are stored; it is determined whether a raw material or in-process item for a first production line among the different production lines satisfies a first determination criterion associated with the first production line; it is determined whether the a material or in-process item which has been determined as not satisfying the first determination criterion satisfies a second determination criterion associated with a second production line; and a raw material or in-process item which does not satisfy the first determination criterion but satisfies the second determination criterion is provided as a recovered item for the second production line.

The application provides a quality mark tracing and propagation method and system based on visual rules, and relates to the technical field of industrial manufacturing quality management. The method constructs a visual rule for determining quality abnormalities and its associated propagation control parameters through a graphical interface, allowing business personnel to flexibly define quality determination logic without relying on programming. When the work-in-process or raw material meets the configured visual rule and is within its effective range, a quality mark is automatically triggered, and the mark is dynamically propagated from the trigger node to the subordinate nodes based on the product structure tree. During the propagation process, the mark intensity of each propagated node is calculated in real time according to a preset intensity calculation model. If the current level exceeds the preset maximum propagation level during the propagation process, the propagation is immediately terminated, and a transaction rollback is performed to cancel the applied quality mark, thereby effectively preventing the excessive spread of abnormal marks and ensuring the consistency, accuracy and stability of the data state.

To provide a carbon dioxide emission calculation system, its method, and its program capable of determining a relationship between an amount of increase or decrease in a difference (total amount difference) between an amount of CO2 emission relating to an entire factory during a predetermined period (a total amount of CO2 emission relating to an entire factory during the period) and the amount of CO2 emission relating to an entire product completed during the period (a total amount of CO2 emission relating to the entire product during the period), and the amount of increase or decrease in a difference between a beginning inventory amount and an ending inventory amount during the period (an ending inventory amount difference), and the amount of CO2 emission relating to the ending inventory amount difference (an ending work-in-progress inventory amount of CO2 emission).SOLUTION: The present invention generates a total amount difference model representing the relationship between the total amount difference and an ending inventory amount difference, based on a pre-prepared total amount of CO2 emission of a factory in each period, the total amount of CO2 emission of a product in each period, and the ending inventory amount, and determines an ending work-in-progress inventory amount of CO2 emission by determining an inventory amount difference contribution ratio of the CO2 emission amount, which is a ratio at which the ending inventory amount difference in the CO2 emission amount contributes to the total amount difference.SELECTED DRAWING: Figure 1

The invention relates to the technical field of production scheduling, and discloses a product scheduling method and device based on a locking event, equipment and a storage medium. The method comprises the following steps of: acquiring products, product information and machine types, and performing multiple sorting on the products according to the sequence that the remaining retention limit time > the priority > the grouping degree; then judging whether the work-in-process violates a judgment standard of a limit judgment rule or not, and if yes, triggering a locking event and locking the machine table or the work-in-process; updating the locking event list according to a time sequence and integrating locking information; the scheduling is iteratively adjusted until convergence based on the real-time locking information. According to the method, a feasible and optimized scheduling plan is generated through a dynamic locking strategy triggered by an event, so that the retention time efficiency, the batch productivity utilization rate and priority conflicts are effectively coordinated, and production delay or resource waste caused by rule conflicts is avoided.

The invention discloses an intelligent prediction closed-loop control method and system for semiconductor manufacturing and a readable storage medium, and the method comprises the following steps: obtaining real-time FDC data of a machine, inputting the FDC data into a preset virtual measurement model, and outputting a first Inline measurement parameter of a product in process; inputting the third Inline measurement parameter into a preset Inline-Wat prediction model, and outputting to obtain a predicted Wat value of the product in process; and when it is judged that the predicted Wat value exceeds an abnormal control range, the Inline measurement parameters of the current station process and the later station process of the product are corrected. According to the semiconductor manufacturing intelligent prediction closed-loop control method and system and the readable storage medium, high-precision prediction, real-time monitoring and process parameter automatic correction in the semiconductor manufacturing process are achieved, the prediction precision and reliability are improved, and the efficient prediction capacity is kept.

A production scheduling method and system for fast board orders, an electronic device and a medium are related to the technical field of data processing. The method comprises: obtaining processing parameters, processing sequences and production data of corresponding processing equipment of each process of a plurality of processes in a fast board order; based on the processing sequence and the completed quantity corresponding to each process, the difference between the completed quantities of adjacent processes is calculated; according to each completed quantity difference, the work-in-process quantity between each process is determined, and the buffer waiting time between each process is set based on each work-in-process quantity; calculate the processing time deviation rate between the workpiece processing time and the corresponding planned processing time of each process, and when there is a to-be-adjusted process whose processing time deviation rate exceeds the deviation rate threshold, the processing start time of the subsequent process is postponed based on the buffer waiting time of the to-be-adjusted process. The technical scheme provided by the present application can improve the overall production efficiency of fast board orders.