25 results about "Work in process" patented technology

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 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.

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 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 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 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.

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.

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.

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.

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.

To reduce a load of progress management of a work in process when manufacturing a product by processing a material in a manufacturing process.SOLUTION: A daily report input means for inputting daily report data including a daily report number, a daily report line number, a process, a facility, a person in charge of work, a work date, a work date, a start time, and a completion time, and registering the daily report data in the storage area, and an inquiry means for outputting inquiry data including a commodity, a sales unit price, a process, a facility, a person in charge of work, a work date, a start time, and an end time for each piece of order detail data by referring to the order data and the daily report data registered in the storage area based on a designated extraction condition.SELECTED DRAWING: Figure 2

The invention discloses a production scheduling optimization method based on dynamic bottleneck identification and balanced distribution, relates to the field of industrial production scheduling and intelligent manufacturing, and aims to solve the problems of overstocked products, insufficient delivery ability, uneven equipment load and the like caused by bottleneck dynamic transfer in multi-variety and small-batch production. The invention provides a production scheduling and resource allocation method based on dynamic bottleneck equalization. The method comprises the steps of firstly constructing an equipment capability group operation time model, then determining work order effective production scheduling weeks and dynamically identifying bottleneck processes of each week, distributing operations based on an expected load rate, if invalid weeks with negative operation distribution quantity exist, removing the invalid weeks and recalculating the invalid weeks, and then updating the model in a rolling manner. And the work order scheduling sequence can be optimized through a quantum genetic algorithm. The method can dynamically respond to bottleneck change, balance equipment load, reduce work-in-process overstock and improve order delivery punctuality rate and equipment utilization rate, and is suitable for flexible production requirements of discrete manufacturing enterprises.

The application relates to an in-process change handling method and device based on a modular data architecture, comprising: obtaining a main line design configuration product structure according to preset basic rules; creating a corresponding change handling sheet for a specific flight change, and identifying a flight validity interval to which the change handling sheet is applicable; determining a design module associated with the change handling sheet of the specific flight change according to a preset flight validity interval calculation rule; identifying an in-process change required to be carried out on the design module by comparing the change handling sheet and the associated design module; and completing single-machine EBOM analysis according to a preset validity calculation rule.

The invention provides a production scheduling optimization method and system, which is applied to a semiconductor production line, is based on a preset process flow, process sections and process steps on the semiconductor production line, and equipment machines for realizing the process steps, and comprises the following steps: determining a first real-time grade of a product in process in each process section; determining a second instant grade of the in-process product in each process section group; a third immediate rating of the articles in process on each equipment station is determined. And a preset scheduling algorithm is adopted, the minimum accumulated waiting time of the work-in-process is taken as a target, and the scheduling production schedule of the work-in-process on the semiconductor production line is determined according to the workpiece processing length, the priority (determined according to the first instant level, the second instant level and the third instant level) and the workpiece interval duration. Therefore, on the premise of considering various production performance indexes, the universality and accuracy of production scheduling are improved, and the production efficiency of the semiconductor production line is improved.

The utility model discloses a high-speed riveting die for multiple metal terminals. The high-speed riveting die comprises a pre-riveting conveying module, a riveting module and a forming module, the before-riveting conveying module is located in front of the riveting module, and the forming module is located behind the riveting module. The riveting module comprises a riveting mechanism, a guide rail and a guide rail seat; the riveting mechanism is positioned above the guide rail and can be used for fixing and riveting a work-in-process on the guide rail; the guide rail seat is positioned below the guide rail and can support and jack up a work-in-process on the guide rail; and the guide rail is fixedly connected with the base body of the guide rail seat. The bimetal terminal riveting die provided by the utility model has the advantages of high automation degree, automatic feeding and high precision; and two times of riveting are adopted, so that the terminal is firm in structure after being formed.

This invention relates to the field of intelligent manufacturing technology, specifically disclosing an intelligent production control system for 3C digital leather cases. The invention extracts product structural feature parameters and process path nodes through a production order parsing module, constructs a dynamic capacity model by combining it with the real-time operating status of the equipment, uses a multi-objective optimization algorithm to solve for the optimal production schedule, and generates quality adjustment instructions based on the defect identification results of work-in-process surface images and the correlation analysis of actual process data. This allows for real-time correction of process parameters or production schedules, solving the problems of capacity mismatch, low equipment utilization, extended delivery cycles, and delayed and inconsistent quality data collection caused by manual production scheduling and experience-based quality judgment. The invention achieves dynamic matching between production plans and actual capacity, improved equipment utilization, shortened delivery cycles, and enhanced real-time feedback and dynamic optimization capabilities for process parameters.

The invention belongs to the technical field of intelligent manufacturing and industrial engineering optimization, and particularly relates to a mixed-flow assembly line material distribution optimization method, which comprises the following steps of: constructing a 0-1 integer programming model taking total cost minimization as a target on the basis of a mixed-flow assembly line structure, material multi-dimensional attributes and human cost difference; and a unique distribution strategy is dynamically assigned to each type of materials through a hybrid solving mechanism combining a cutting plane method and a column generation algorithm. By the adoption of the technical scheme, the sorting cost, the carrying cost, the extra operation cost of assembly personnel and the inventory holding cost of products in process can be optimized in a collaborative mode, and global cost optimization is achieved while production continuity is guaranteed.

The invention discloses an automatic scheduling method for a glass bracket production plan based on production-sales-stock data, and relates to the technical field of production plan scheduling, and the method comprises the steps: firstly obtaining a production scheduling work order containing production quantity and delivery time, setting a key process taking a batch as a tracing unit, and associating the production scheduling data of the production scheduling work order and the key process to generate a work order batch integration list; the work order number and the piece number related to the batch are extracted, and the batch exposure is calculated; on the basis of production-marketing-stock data and equipment clamping constraints, generating a single-work-order candidate batch combination scheme set, a cross-work-order candidate batch combination scheme set and a product-in-process candidate batch combination scheme set, splitting batches, calculating candidate batch exposure quantities, accumulating the candidate batch exposure quantities to obtain the total exposure quantity of all the schemes, and selecting the scheme with the minimum increment as the current optimal batch scheme after the exposure quantity increment is obtained through calculation; and finally, ascending according to the candidate batch exposure quantity, and ascending according to the same exposure quantity according to the work order delivery time to form a final production sequence. According to the invention, a basis is provided for glass bracket production scheduling, and the production efficiency and the quality tracing risk of the glass bracket are balanced.

The invention relates to a method and system for predicting the position of a product in process on a friction line, and belongs to the technical field of vehicle production. The method comprises the following steps: at an inlet of a friction line, obtaining a product identifier of a product entering the friction line; the product identifiers of all the in-process products are sequentially stored according to the sequence of entering the friction line, a in-process product sequence is generated, and the distance interval of all the in-process products in the in-process product sequence is a preset station interval; acquiring a conveying operation signal of the friction line; according to the conveying operation signal, the predicted position of each work-in-process in the work-in-process sequence is calculated; and when the work-in-process passes through a preset calibration station, correcting the predicted position of each work-in-process in the work-in-process sequence according to the position of the calibration station. According to the invention, the position of the whole friction line on the product can be accurately tracked under the condition that only a small number of recognition devices are arranged.

The invention belongs to the technical field of production plan scheduling, and discloses a production plan scheduling method and system of an MES system. The method comprises the following steps: preprocessing production order data to generate a comprehensive scheduling value priority sequence; based on the comprehensive scheduling value priority sequence, compatibility combination groups are sequentially obtained, a scheduling scheme population is constructed, multi-objective evolution fitness is calculated based on the delivery time satisfaction rate, the switching cost, the work-in-process turnover time and the repair risk index, non-dominated sorting, congestion distance evaluation, selection, intersection and mutation operation are executed, and an optimal scheduling scheme is generated; verifying the optimal scheduling scheme, and generating a verification passing scheme set and a verification failing scheme set; if the verification is not passed, returning to re-optimize, and if the verification is not passed, generating a scheduling instruction and issuing and executing the scheduling instruction; the production scheduling accuracy is improved, the order response period is shortened, and the process switching burden is reduced.

A method, a system(2),a computer-readable storage, a medium and a computer program product for monitoring WIP status in production and an industrial system are provided. The method includes: during a production process, an operation status signal of a production device which is used for processing a WIP object is obtained from a field control device(101); according to the operation status signal of the production device, an operation behavior of the production device is simulated and operation information related to a status of the WIP object is determined(102); according to the operation information, a changing behavior of the WIP object is simulated(103); the operation behavior of the production device and the changing behavior of the WIP object are visualized(104). The technical solutions of the application can achieve full process monitoring of the WIP status during the production process.

The application relates to the technical field of industrial manufacturing management, and discloses a production planning method based on industrial big data, which comprises the following steps: configuring a production line station as an autonomous node; each node acquires the physical accumulation degree of a work-in-process queue in real time through a state sensing device and generates a back pressure value; periodically broadcasting a data packet containing a node identifier and a back pressure value to an upstream; and dynamically distributing workpieces to a node with the lowest load according to the received downstream back pressure value. The application realizes autonomous balancing of a production line load through a decentralized back pressure value propagation mechanism, accurately maps the future time load of the back pressure value by combining standard working hour weighted calculation, and introduces a trend code to predict the state change of a node, so that a distributed production control system with real-time response and decision-making capability is formed.

The invention relates to the field of industrial data processing and management, and discloses a process path optimization system for a flexible OLED (Organic Light Emitting Diode) manufacturing process, which comprises a module for acquiring commercial economic attributes, physical states and available path options of work orders of work-in-process, a fusion module for generating economic attribute dynamic portraits of the work orders of work-in-process for the path options, and a processing module for processing the dynamic portraits of the work orders of work-in-process. The decision module is used for caching portraits and subscribing key physical resource state change events, and when it is diagnosed that the economic attribute of the original optimal process path is inferior to other available process path options in the cache due to the occurrence of the events, the decision module is used for selecting the optimal path according to a preset optimization strategy. According to the method, the dynamic guarding mechanism with decision timeliness is established, the decision can be automatically cancelled and re-optimization can be triggered when the original optimal decision fails due to sudden change of the physical environment, and the management risk of high-value work orders is avoided.