46 results about "Manufacturing management" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a cloud edge collaborative production manufacturing management system based on artificial intelligence, which comprises a task scheduling optimization module, a resource scheduling and load balancing module, a production efficiency evaluation module, a dynamic load adjustment module and a fault tracing analysis module. According to the invention, by accurately analyzing the task dependency relationship, optimizing the task scheduling strategy and improving the response sequence and delay control of task execution, the processing bottleneck problem caused by data transmission delay is avoided, the task execution period is flexibly adjusted, the high-frequency tasks in the production process are effectively managed, and the load unevenness caused by fluctuation is reduced; the method can accurately identify and regulate the production bottleneck, improve the adaptive capability of the production process, accurately identify the potential fault source, shorten the fault diagnosis time, reduce the complexity, more rapidly position the problem, improve the stability, efficiency and fault tolerance of the production system, and integrally enhance the self-optimization capability of the production system.

The invention relates to the technical field of cloud ERP intelligent manufacturing management, in particular to an ERP production management system based on cloud collaboration, which is used for solving the problems that in the prior art, orders cannot be disassembled into executable tasks with constraints, resource capacity, real-time load, material inventory and historical data cannot be fused to generate and issue an optimized scheduling scheme, and the scheduling efficiency is high. And full-process dynamic collaboration of scheduling, matching and feedback is difficult to realize. An order is disassembled into executable tasks with constraints through a dynamic optimization scheduling module, an optimization scheduling scheme is generated and issued by fusing resource capability, real-time load, material inventory and historical data, a state is collected in real time during execution, local or global rescheduling is triggered according to a deviation range, inventory constraints are dynamically updated, and the scheduling efficiency is improved. An execution result is written into the database in a closed-loop mode, and full-process dynamic cooperation of scheduling, matching and feedback is achieved.

The invention relates to the technical field of industrial management, and discloses a composite material sharing intelligent manufacturing management system based on an industrial internet, which comprises a cross-layer state modeling and management and control strategy mapping module, the method comprises the following steps: firstly, based on multi-source process data, respectively quantifying the local cumulative strength of microdefects, the association degree of a macroscopic equipment operation sequence and the defect connectivity risk degree driven by space geometry, and aggregating the indexes into cross-layer self-organizing critical coupling state parameters; and then generating a resource scheduling weight vector and a geometric complexity load scaling coefficient through deterministic calculation, and dynamically adjusting a task allocation probability and an equipment bearing limit according to the resource scheduling weight vector and the geometric complexity load scaling coefficient. According to the method, active avoidance and decriticalization control over the critical risk of the manufacturing system are achieved, and high-quality and efficient collaborative production of complex components is guaranteed.

The application relates to the field of automobile production and manufacturing management, and discloses an automobile production process management method, system, terminal, medium and program, through establishing an automobile production process management framework, the whole automobile production process is sequentially executed according to the management framework, the automobile production process management framework can realize the transparency of the whole production process, an MES main production plan is prepared according to the automobile production process management framework, production scheduling is determined, a production plan is established, scheduling is carried out according to the production plan, the management process can be effectively followed up in time, a business process is designed according to the production scheduling, workshop execution data, procurement management data and warehouse management data are sequentially acquired at a business function layer, and the workshop execution data, the procurement management data and the warehouse management data are stored into an automobile production process management database, automobile production process management work is completed, information interaction in automobile production process management is ensured, and the control over the whole process information is improved.

The invention discloses a multi-field die cutting part intelligent manufacturing management system and method based on the industrial internet, and relates to the technical field of data analysis, and the method specifically comprises the steps: building a product feature set based on the product demands of die-casting die cutting parts, and calculating the product passing rate of each die-casting die; extracting die-casting dies greater than a preset first qualified threshold value, obtaining pouring features, recording the pouring features as a die feature set, obtaining die features influencing die-casting die cutting piece production based on a correlation algorithm, and recording the die features as key influence die features; and for any die feature in the die feature set, based on the number of times of taking the die feature as the key influence die feature, obtaining a product feature of taking the die feature as the key influence die feature, and counting a constraint set of the die feature, thereby obtaining an optimal feature value range, and selecting an optimal die-casting die according to the optimal feature value range. The key influence mold features are identified based on the correlation algorithm, and the limitation that the quality of the mold is judged only depending on experience or a single parameter traditionally is broken through.

The application relates to the technical field of hardware manufacturing management, in particular to a digital MES management system based on hardware manufacturing. The system comprises the following modules: an order receiving module, an on-site environment collecting module, an adaptive relationship analyzing module, an execution sequence distributing module and a production scheduling module. The order receiving module is used for receiving hardware manufacturing orders and decomposing discrete process parameters of each hardware; the on-site environment collecting module is used for collecting equipment state data and material inventory data and deducing process executable capacity parameters; the adaptive relationship analyzing module is used for determining process candidate sets on different equipment; the execution sequence distributing module is used for distributing process execution sequences of different hardware; and the production scheduling module is used for coupling and adjusting process execution sequences of each hardware to obtain a rolling production scheduling sequence. The application improves the on-time delivery rate of orders and the overall operation efficiency of the production system under the premise of ensuring process constraints and equipment utilization.

The invention relates to the technical field of production and manufacturing management, and discloses a multi-source demand fusion and dynamic scheduling production plan collaborative management method and system, and the method comprises the steps: automatically obtaining production demands from a plurality of heterogeneous business source systems through a unified interface; performing data mapping and conversion on the heterogeneous demands to generate standardized main production plan demands, and distributing a unique tracing identifier penetrating through a production full link for each demand; calculating and generating a main production plan through a dual-mode scheduling engine supporting forward and reverse scheduling algorithms on the basis of a standardized demand, a product production cycle parameter and a working calendar; and issuing the main production plan to a manufacturing execution system, automatically capturing demand changes, performing versioning processing on affected plan items, and re-triggering scheduling calculation. According to the invention, end-to-end monitoring is realized through the unique tracing identifier, and rapid and closed-loop response is carried out on demand change, so that the accuracy and agility of production plan making are improved.

This invention provides a logging method, logger, and storage medium, applicable to Enterprise Resource Planning (ERP) systems, such as manufacturing management systems, supply chain systems, human resources systems, financial systems, tax systems, and auditing systems. The method includes: extracting hierarchical relationships from objects to be processed; mapping each level in the hierarchical relationship to a node; organizing the nodes into a hierarchical structure for storage according to the hierarchical relationship; and using tags to record log information for each node in the hierarchical structure.

The present application relates to the field of industrial internet of things and intelligent manufacturing management, in particular to a personnel off-duty intelligent detection method based on UWB positioning. The method comprises: obtaining a UWB trajectory sequence of personnel and position coordinates of a target object; filtering the UWB trajectory sequence to obtain a target trajectory sequence; performing feature extraction based on the position coordinates of the target object and the target trajectory sequence to obtain a moving threshold, a radial velocity and an activity concentration; constructing a risk assessment model; taking the moving threshold, the radial velocity and the activity concentration as inputs of the risk assessment model to output a risk index; and generating a personnel off-duty detection result according to the risk index. In this way, personnel off-duty behavior can be more comprehensively and accurately judged.

1. The name of the design product: industrial router (industrial S10 series). 2. The use of the design product: for an industrial router, with built-in embedded memory, supporting local storage and data processing; can be used for remote control, remote operation and maintenance, industrial manufacturing, PLC management, logistics sorting, etc. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best shows the design points: perspective view 1.

An artificial intelligence decision support system for manufacturing management is disclosed, which is implemented as a computer system that comprises a data warehouse and a host device, wherein the host device includes a processor and a storage device. In particular, the storage device stores an application program, and the processor executes the application program to perform the following functions: displaying a user operation interface on a display of a client device; converting natural language content and / or unstructured data provided through the user operation interface into a semantic embedding vector; processing the semantic embedding vector to obtain a task objective; activating a corresponding intelligent agent module to perform a retrieval-augmented generation (RAG) operation in the data warehouse based on the task objective to produce RAG data; and activating a large language model to generate a natural language specification and / or a visualization specification according to the RAG data.

The invention discloses an industrial robot multi-machine cooperative intelligent manufacturing management and control system and method, and the method comprises the steps: collecting the bottom physical state data of a plurality of robots in real time through a sensing monitoring accessory set based on a global nanosecond clock network; constructing a rigid-flexible coupling interference matrix to perform forward kinematics solution, and separating a total displacement error into a dynamic elastic deformation error and an external disturbance contact force to realize joint-level decoupling; calculating a global coupling compensation amount containing a feed-forward current pulse compensation sequence based on the dynamic elastic deformation error; kinetic energy differential is calculated in combination with the speed and the load to generate a dynamic anti-collision safety threshold, and predictive feed-forward compliance compensation is triggered and a current pulse is issued when the dynamic threshold is approached. According to the invention, inertia sudden stop caused by mistake under heavy-load and high-speed working conditions can be shielded, and self-adaptive flexible concession is realized before collision, so that the safety anti-collision capability and the operation precision of multi-machine cooperation are obviously improved.

The invention relates to intelligent manufacturing management, in particular to an industrial automatic intelligent manufacturing management system based on a block chain technology, which comprises a base, a plurality of storage parts are arranged on the base, a rotating bracket is rotationally connected to the base, a winding drum is rotationally connected to the rotating bracket, and a steel wire rope is stored on the winding drum. A lifting support is slidably connected to the rotating support, the end of the steel wire rope is fixedly connected to the lifting support, and a model part and a weight part are arranged on the lifting support. And parts of different models can be stored in order according to the material removing machining technological process.

The application relates to the technical field of industrial communication and intelligent manufacturing system integration, and discloses an enterprise-based intelligent manufacturing digital integration analysis method, which comprises the following steps: collecting real-time operation data of production equipment, forming an integrated data pool after cleaning, standardization and space-time alignment, constructing an enterprise intelligent manufacturing knowledge graph based on the business logic of the intelligent manufacturing field and calculating key performance indicators, combining the integrated data pool and the knowledge graph, applying the integrated data pool and the knowledge graph to constructing a digital twin model, predicting production bottlenecks and evaluating different production scheduling plans, and finally generating a comprehensive analysis report based on the index calculation results and the simulation results. The application realizes full-process automation from data perception, integration and application to decision output, and significantly improves the transparency, analysis accuracy and decision intelligence level of the manufacturing management process.

The present invention relates to creating a manufacturing result for each manufacturing management unit on the basis of a preset condition from manufacturing data, which is time-series data acquired from a device. This manufacturing information management device is provided with: a manufacturing data acquisition unit that acquires, from a device, manufacturing data including at least time-series data relating to manufacturing; and a manufacturing result creation unit for creating, as a manufacturing result, manufacturing data for each of the arbitrary manufacturing management units from the manufacturing data on the basis of a preset manufacturing result creation condition.

The application relates to the technical field of manufacturing management, and particularly discloses a manufacturing execution management system based on big data, which comprises an efficiency collection module, an efficiency monitoring module and a device management module. The efficiency collection module sets a recording interval, divides the manufacturing process of a product into a plurality of links, obtains the processing amount of each device in the current link and the previous link in the recording interval, and calculates the processing amount. The efficiency monitoring module obtains the total efficiency of all devices in the current link in the recording interval, fits the current link efficiency curve and the previous link efficiency curve, and obtains a real-time accumulation amount at each end time. The device management module obtains the change rate of the accumulation amount curve, and if the change rate is greater than 0, it indicates that there is an efficiency difference. The device management module obtains the efficiency change curve of each device in the current link, calculates the single-device change rate, compares the single-device change rate of each device in the current link with the change rate of the accumulation amount curve, and selects the device in the current link that needs to be overhauled.

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 invention relates to the technical field of manufacturing management, in particular to a socket automatic plugging control management system and method, and the system comprises a clamping action recognition module, a control sequence revision module, a signal combination verification module, an abnormal record merging module and a dispatch table content updating module. According to the method, rapid identification and recording of response deviation are realized through numbering and filing of triggering and feedback intervals, association characteristics of abnormal action are verified and judged through feedback combination by adjusting a control instruction sequence, eliminating signal triggering conflicts and time sequence dislocation, and failure point locations and corresponding logics are clarified; according to the method, the repeated fault mode of the device is extracted through abnormal combination, merging, analysis and extraction, the continuous recognition capability of abnormal behaviors is improved, the structural consistency between control logic and task execution is ensured by synchronously revising a scheduling instruction sequence, and the stability and accuracy of a plugging process under a complex working condition and the closed-loop execution capability of scheduling response are enhanced.

The invention provides a flow shop scheduling dual-objective optimization method based on a bacterial foraging optimization algorithm, and relates to the technical field of production and manufacturing management. The method specifically comprises the steps of obtaining original data of production scheduling in an industrial assembly line, and constructing a coupling coding scheme based on a task sequence and a delay period matrix under the background of time-of-use electricity price; an initial population is generated by adopting a hybrid heuristic initialization strategy, wherein each individual represents a complete scheduling scheme through a coupling coding strategy based on a task sequence and a delay matrix. According to the method, global optimization is carried out on a solution space based on an improved multi-target bacterial foraging optimization algorithm, and finally, an optimal individual is output and serves as an optimal scheduling scheme of a flow shop. According to the method, the problem of double-target optimization faced by original equipment manufacturers in flow shop scheduling under a time-of-use electricity price strategy is effectively solved, and meanwhile, the production efficiency is improved and the electric charge expenditure is reduced.

Techniques of item assembly testing that include: obtaining assembly information; generating, based on the assembly information, a bill of materials (BOM) including a listing of parts corresponding to a configuration of parts defined by the assembly information; generating, based on the listing of parts, a set of reference designators; obtaining an item label set; generating, based on the item label set and the set of reference designators, an assembly bill of materials (ABOM) that includes the labels associated with the parts reference designators; generating, for a test of the item assembly, a record of the ABOM; obtaining test data for parts of the item assembly (the item assembly tested having a configuration specified the ABOM); and generating a test results ABOM including test data for the parts associated with the labels for the reference designators associated with the parts.

The invention discloses an intelligent monitoring and early warning system and method for gaseous pollutants in semiconductor manufacturing, and relates to the technical field of semiconductor manufacturing management.The intelligent monitoring and early warning system comprises a window screening module, a curve construction module and a real-time early warning module; calculating an exclusive differential pressure attenuation period, and meanwhile, locking a window with the highest risk score through dynamic time window division; the curve construction module disturbs phase alignment through pressure difference, divides effective disturbance sections and synthesizes a window total pressure difference curve; and the real-time early warning module calculates the disturbance accumulated impulse of the window total pressure difference curve, and performs early warning linkage by combining with the predicted pollutant permeation amount. According to the method, the pressure difference superposition risk of multiple door opening can be accurately identified, the accuracy and pertinence of monitoring and early warning are improved, and gaseous pollutant permeation is effectively prevented and controlled.

To efficiently set priorities for investigating data locations that may lead to business improvements or new service creation.SOLUTION: In a manufacturing management support method of supporting manufacturing management of products that involve multiple processes, actual data that associate time, product ID, and process ID that identifies the process where the product with the product ID was staying at the time is arranged in a coordinate system with a first axis and a second axis as coordinate axes. Then, from a production planning line for each product ID, which connects the actual data for each product ID with a straight line, specific data location candidates that includes turbulent flow locations where the production planning line adjacent to the first axis direction is not parallel to the second axis direction are extracted, and the production planning lines that include at least part of the same specific data location candidate are extracted and grouped. Based on a time width indicating width for each process of a specific data location area that defines area for setting a new production planning line for laminarizing the production planning line based on the grouped production planning lines, investigation priority for the specific data location area is set.SELECTED DRAWING: Figure 4

The invention relates to the field of machine tool manufacturing, in particular to a machine tool manufacturing management system and method based on the Internet of Things, and the system comprises a meta-action input module, a displacement error module, a quality modeling module, a control prediction module and a scheme output module, the meta-action input module is used for generating a meta-action sequence, and the displacement error module is used for outputting a machining error model; the quality modeling module is used for carrying out geometric error modeling on a workpiece, the control prediction module is used for predicting the actual position of a feeding shaft and setting the PLC weight, and the scheme output module is used for generating and screening a PLC adjustment scheme, the machining quality can be monitored and predicted in real time, the machining process is optimized, and the machining precision and efficiency of a machine tool are improved; the modeling precision of the workpiece in the machining process of the machine tool is improved, the machining precision and the assembly quality are improved, the feedback process is optimized, the machining loss and the energy consumption cost are reduced, and the quality control capacity of precision machining is improved.

The invention relates to a chemical product production plan optimization method and device, equipment and a storage medium, and belongs to the field of chemical manufacturing management. The method comprises the following steps: acquiring a chemical product production recommendation sequence set of all production devices in a current production plan; obtaining a chemical product production extension sequence set of the current production device; repeating the expansion operation of the chemical product production recommendation sequence set of the current production device until a chemical product production expansion sequence set of all production devices in the current production plan is obtained; optimizing the chemical product plan based on the actual production constraint condition, the objective function, the decision parameter, the optimization purpose and the chemical product production extension sequence set of all production devices in the current production plan; and obtaining an optimized chemical product production plan based on an optimization result. According to the method, various complicated production practices of refinery enterprises can be handled, and an optimized chemical product production plan is given in a minute-level time range.

The application discloses a kind of based on production condition perception's advertisement material production scheduling method and system, it is related to advertisement material manufacturing management technical field.The kind of based on production condition perception's advertisement material production scheduling method and system, including S1, collection and pre-processing advertisement material production process in execution time related data and resource queue data, constructs standardization production condition dataset;S2, analysis rework influence in production process in the degree of conduction, adjustment execution arrangement in production process;S3, assesses the rhythm stability degree of production process, linkage constraint production execution arrangement in time window occupancy and task release rhythm;S4, assesses the adjustable degree of task time landing point, corrects task plan start time.It solves the problem that existing advertisement material production management relies on single task average duration scheduling, lacks the depiction of rework timing aggregation and cross-node conduction disturbance, leading to scheduling deviation accumulation and causing resource occupancy imbalance.

To support manufacturing based on the correlation of manufacturing process information and a plurality of pieces of sensing information in a manufacturing device having a plurality of sensing devices.SOLUTION: Executing a process of acquiring identification information of an article manufactured by a manufacturing device, manufacturing process information of the article, first sensing information obtained from a first sensing result of the article by a first sensing device, and second sensing information obtained from a second sensing result of the article by a second sensing device, and a process of generating a learned learning model of machine learning based on the manufacturing process information, the first sensing information, and the second sensing information; An information processing device that generates a first learning model that inputs information corresponding to manufacturing process information and outputs prediction results of first sensing information and second sensing information, and a second learning model that inputs information corresponding to the manufacturing process information and outputs a value representing a correlation between the first sensing information and the second sensing information.SELECTED DRAWING: Figure 1

The invention relates to the technical field of sensor manufacturing management, and discloses a flexible friction electric pressure sensor manufacturing management system based on warp-knitted spacer fabric. The system comprises three core modules, namely a manufacturing monitoring module, a quality analysis module and a decision execution module, wherein the manufacturing monitoring module tracks the whole production process of the warp-knitted spacer fabric in real time and collects fabric structure parameters, coating thickness distribution data and electrode layer fitting state data; the quality analysis module performs real-time correlation analysis on the multi-dimensional data and integrally judges whether the manufacturing process has process abnormality or not; when the decision execution module detects abnormity, a process regulation and control strategy is automatically matched according to a specific abnormity type, and manufacturing equipment parameters are dynamically adjusted according to the process regulation and control strategy. According to the system, key data integration in the whole manufacturing process is achieved, the process abnormality judgment accuracy and response efficiency are improved, unqualified products are reduced, material waste is reduced, shutdown loss is avoided, stable performance of the sensor is guaranteed, and the system is suitable for large-scale production.

The invention provides a contract production measuring and calculating method and system based on cold-rolled products. The method comprises the steps that contract information transmitted by a manufacturing management system is acquired, and production process parameters of each unit are acquired; searching a rule static maintenance table to obtain the production speed of each unit; according to the production speed and the production process parameters of each unit, the producible time length of each unit is calculated based on a production time length measuring and calculating model; and summarizing the producible duration of each unit, and calculating a contract completion node of each unit in combination with maintenance and batch information. According to the method, a set of scientific cold-rolled product contract production measuring and calculating scheme is formulated by establishing the measuring and calculating model of the production time of each cold-rolled unit and fully considering the influence of each related factor, and the contract production progress in actual production is relatively high in matching degree with the measured contract production progress; the contract production duration and the predicted completion date of each unit can be visually seen, and the contract fullness condition of each unit can be known.

This application relates to a document processing method, apparatus, computer equipment, and storage medium, applicable to Enterprise Resource Planning (ERP) systems, including but not limited to manufacturing management systems, supply chain systems, human resources systems, financial systems, tax systems, and auditing systems. The method includes: when a document is opened, loading and caching the document data, returning the main document pagination data from the document data, so that the main document pagination data is displayed on the main document interface after the document is opened; the main document interface has at least one corresponding sub-document interface, different sub-document interfaces are used to display different categories of data in the document data; when a target sub-document interface is triggered to display, retrieving the sub-document pagination data under the target category corresponding to the target sub-document interface from the cached document data, so that the sub-document pagination data under the target category is displayed on the target sub-document interface. This method can reduce computer overhead.

The invention provides a method for transmitting process steps in a quality tool, relates to the technical field of manufacturing industry management and data processing, and realizes automatic transmission of the process steps and special characteristics among APQP, PFMEA, CP, DFMEA, MSA and SPC modules by fusing a bill of materials, production parameters and process images to generate an adaptive semantic association model and combining an adaptive dynamic topology network. According to the process steps, an APQP flow chart is generated and then automatically transmitted to a PFMEA generation report, special characteristics are imported into APQP from a DFMEA list and marked on CP, and finally the APQP is output to an MSA / SPC module to generate a measurement task and a process capability report, so that the consistency of data in different files is ensured. Compared with a fixed transmission path described by a file, by dynamically adjusting the network topology weight, monitoring the data integrity in real time and automatically repairing format errors or omissions, an automatic transmission mechanism remarkably improves the collaboration efficiency between modules, and errors caused by manual intervention are reduced.