81 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 casting sand production, and discloses an artificial intelligence-based casting waste sand recycling intelligent manufacturing management system, which comprises a data acquisition module for acquiring production data in a casting waste sand treatment process; the data processing and analyzing module is used for receiving the data transmitted by the data acquisition module and processing and analyzing the data; the intelligent control module is used for adjusting operation parameters of crushing, grinding, sorting and roasting equipment; the production scheduling and management module is used for receiving the control information, making a production plan and performing inventory management; and the quality detection and tracing module is used for establishing a quality tracing system and ensuring the traceability of the data. By constructing a data processing and modeling module based on artificial intelligence, dynamic identification and adjustment optimization of a key process state in a foundry waste sand treatment process are realized, so that the adaptability of the system to complex working conditions is improved.

The invention belongs to the technical field of semiconductor manufacturing management, and particularly relates to a semiconductor manufacturing-oriented gaseous molecular pollutant online monitoring system, which comprises a small-size traceability dynamic historical influence analysis module, a multi-source real-time monitoring and early warning module, a prospective early warning analysis module, an active control detection module and an intelligent feedback interface, the small-size traceability dynamic historical influence analysis module is based on a hybrid model, historical data and real-time data are combined, the future influence trend of pollutants can be predicted and graded, and therefore enterprises can make coping strategies in advance, reasonably arrange production plans and reduce the influence of the pollutants on the product yield. According to the method, pollutant diffusion paths can be simulated, high-risk stations can be positioned, pollutant sources and propagation paths are analyzed through the Bayesian network, and a root cause probability sorting list is output, so that a basis is conveniently provided for prevention, control and treatment of pollutants, and targeted measures can be quickly and accurately taken.

The invention discloses an electric furnace production line production plan automatic optimization scheduling method and system, and belongs to the field of automatic control. Actual production data and manufacturing standard data are collected from a manufacturing management and production process control system, and different steel grade characteristics and time standards are determined; extracting and importing a to-be-discharged heat plan from a manufacturing management system; according to a process rule condition and a shutdown maintenance plan, setting a production plan automatic scheduling constraint condition; determining an optimal production plan sorting result on the basis of a genetic algorithm model according to the plan of the heat needing to be arranged and the constraint condition; and according to the steel grade plan sequence and the corresponding time standard, with the process with the longest processing period as the center, the production time table of each process is determined in sequence, and the predicted yield, time consumption information and risk warning information in the plan period are displayed. The scheduling optimization model is used for calculation, the optimized scheduling of the production plan is completed, the planned yield, the planned time consumption and the planned risk point warning are calculated, and the scheduling efficiency, precision and intelligent level of the production plan of the electric furnace production line are improved.

The invention relates to the technical field of production management, and discloses a production management method based on MES intelligent manufacturing, which is used for solving the problem that an original quality control standard is no longer representative during manufacturing management, and comprises the following steps: obtaining a resource state data set and production task plan data, generating task instruction data, and storing the task instruction data in a database; continuously collecting execution process data, obtaining a production process influence factor, evaluating according to the production process influence factor to obtain a quality standard adjustment driving index, judging whether the current process control standard needs to be adjusted or not, and if the current process control standard needs to be adjusted, dynamically adjusting the initial process control standard, the actual process control standard is obtained, whether quality deviation or parameter abnormity exists or not is judged according to the execution process data and the target value and tolerance range in the actual process control standard, the quality qualification rate of product production is effectively improved, and the accuracy of production management is improved.

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 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.

To manage a manufacturing system further appropriately using collected setting values and operation situations.SOLUTION: A history information management system for managing manufacturing history information regarding manufacturing products in a manufacturing system having a plurality of facilities 4 includes: a plurality of related devices regarding manufacturing the products; and a history information management device 1 which is connected to the plurality of related devices. The history information management device 1 includes: a history information collection unit 12 which collects manufacturing history information indicating manufacturing history in the plurality of facilities from the plurality of related devices; an influence range identification unit 132 which identifies an influence range on the manufacturing system by the manufacturing history indicated by first manufacturing history information in a first facility when the first manufacturing history information is collected; and a manufacturing management unit 133 which performs manufacturing management processing for manufacturing in a second facility included in the influence range according to the first manufacturing history information.SELECTED DRAWING: Figure 16

To provide a disaster prevention equipment production management system capable of providing assistance in efficient production management for disaster prevention equipment.SOLUTION: A disaster prevention equipment production management system is provided, comprising an acquisition unit for acquiring a delivery date of disaster prevention equipment and a completion date of a specific step for producing the disaster prevention equipment, and a schedule generation unit configured to generate a production schedule including multiple steps for producing the disaster prevention equipment in such a way that the specific step is completed on the completion date before the delivery date.SELECTED DRAWING: Figure 5

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.

The invention discloses a supplier production and financial post-loan supervision system based on execution of manufacturing management. The system comprises a fund monitoring module, a risk assessment module, a warning module and a regulation and control module. The fund monitoring module is used for monitoring fund of an electronic account and mastering the flow direction of the fund every time, and the fund monitoring module comprises an amount calculation module, an information identification module, an identity acquisition module, a loan module and a prompt module; the amount calculation module is used for calculating the remaining funds of the electronic account, and calculating the remaining funds during each fund transfer; and the information identification module is used for identifying flow direction information when the enterprise needs large fund circulation, and loaning can be carried out only when the requirement is met. According to the method and the system, the fund trend of the customer can be controlled, so that the customer is ensured to invest the fund into the development of an enterprise instead of personal consumption, the possibility of customer fund chain breakage is reduced, and the loan risk is reduced.

The application relates to the technical field of die manufacturing management, in particular to an ERP cloud management system and method applied to precision die intelligent manufacturing. The method comprises the following steps: collecting basic production data of a whole die machining process; respectively acquiring a tool wear amount change curve and clamping positioning deviation data in a machining batch; combining the tool wear amount change curve and the clamping positioning deviation data to judge machining stability of the machining batch, and calculating a dynamic correction range of process parameters of a to-be-machined die; using the dynamic correction range of the process parameters, combining a current machining equipment load state and a tool life remaining value, and generating a corrected process parameter set; detecting actual size data of the to-be-machined die after machining, and performing point-by-point comparison to iteratively correct the process parameters. The application enhances controllability and stability of an automobile die machining process, reduces manual intervention and waiting time, and improves equipment utilization and product quality consistency.

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.

The invention discloses a vehicle module generalization rate management method and system, a medium and a product, and the method comprises the steps: obtaining the type selection demands of N part modules of a target vehicle, and carrying out the matching of the type selection demand of each part module in the N part modules with a candidate part module in a preset part library; based on a matching result, a target part module is determined from the N part modules, and candidate part modules in the preset part library cannot completely meet the type selection requirement of the target part module; determining an optimization strategy of the target part module based on a logistics management condition, a material management condition and a manufacturing management condition corresponding to the target part module; and if the optimization strategy is an executable strategy, determining an overall part generalization rate index of the target vehicle based on the relationship between each part module and the candidate part module in the preset part library, and the scheme is beneficial to management control of the vehicle module.

The invention discloses a disposable underpants manufacturing management system based on production equipment state analysis, relates to the technical field of manufacturing management, and solves the problem of insufficient real-time performance caused by network transmission delay due to the fact that a traditional equipment monitoring system generally adopts a centralized data processing architecture in the prior art. Yield, abnormal information, efficiency, shutdown duration, temperature, pressure, conveying speed and visual data are obtained in real time; the state analysis module analyzes the equipment state through machine learning and calculates the health degree; the dynamic control module adjusts operation parameters according to the health degree; and the quality association database is used for storing the mapping relation between the equipment state and the quality index and triggering quality tracing.

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.

A cell manufacturing management platform facilitates management of a cell manufacturing process. The cell manufacturing management platform tracks events associated with a cell manufacturing process and coordinates between disparate entities involved in the process. The cell manufacturing management platform utilizes machine learning techniques to generate inferences associated with event scheduling in a manner that optimizes an efficiency metric and reduces likelihood of exceptions occurring. Machine learning models may furthermore be used to generate various alerts or other actions associated with the process. A user interface enables different participating entities to track progress of the process and upcoming events.

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.