10 results about "Manufacturing process management" patented technology

The invention belongs to the technical field of production testing in the manufacturing industry, and particularly discloses a production testing system and method based on the manufacturing industry, a terminal and a medium, and the system comprises a back-end service layer which is in communication connection with a front-end service layer for information interaction, dividing the business logic of the production test into a plurality of business modules which can be independently managed through a module registration mechanism; the equipment interaction layer comprises an equipment driving interface, at least one equipment driving plug-in and an equipment communication unit; and the system integration layer is used for carrying out information exchange with a manufacturing execution type service system through a standardized data interaction mode so as to obtain and send the production test data. Test data is aligned with a manufacturing execution type business system through a standardized data interaction mode, connection between a production test link and manufacturing process management is realized, and the overall cooperative capability of a production line is improved.

A battery manufacturing process management system according to an embodiment includes a battery cell dummy configured to obtain data corresponding to a battery manufacturing process and a controller configured to control at least one process variable related to the battery manufacturing process based on the data corresponding to the battery manufacturing process that is obtained using the battery cell dummy.

The invention discloses a functional fiber yarn spinning manufacturing process management system, which relates to the technical field of spinning management, and comprises the following modules: a drafting force monitoring module for acquiring drafting force through a piezoelectric force sensor mounted at a roller bearing seat, drawing a drafting force real-time oscillogram through the drafting force, setting a drafting force range preset threshold value, and outputting a drafting force real-time oscillogram through the drafting force real-time oscillogram; analyzing the drawing force real-time oscillogram through a drawing force range preset threshold value to obtain a drawing force analysis result; according to the scheme, the corresponding abnormal reason is captured in the visual monitoring result through the change direction of the drafting force in the comprehensive judgment module, and if the abnormal reason is captured, the judgment result is accurate, so that the spinning abnormal condition can be known at the first time when the data is abnormal, and the working efficiency is improved. Therefore, the broken end phenomenon of the drawn sliver due to long-time stretching under abnormal data can be avoided, and the situation that the drawn sliver does not reach the standard after being stretched into roving can be avoided.

The invention discloses a process management method, device and equipment based on frame manufacturing and a medium. The method comprises the following steps: acquiring a target to-be-managed vehicle frame in real time, and acquiring a target identity label quick response code corresponding to the target to-be-managed vehicle frame; in the frame manufacturing joint process, recording and detecting the process information of the target to-be-managed frame through a preset frame manufacturing process management method according to the target identity identification quick response code to obtain a target process information record and detection result; and feeding back the target flow information record and the detection result to a user in real time, so that the user performs flow management operation on the target to-be-managed vehicle frame in the vehicle frame manufacturing process according to the target flow information record and the detection result. The problems that due to the fact that manual recording is relied on, processes are omitted, numbers are omitted and wrong numbers are omitted, the frame manufacturing process cannot be controlled in real time, and tube arrangement and tracing cannot be effectively carried out are solved, and the accuracy, efficiency and flexibility of frame manufacturing process management are improved.

The invention discloses a manufacturing process management system and method for a starting mix packaging production line, and belongs to the technical field of intelligent manufacturing and industrial informatization. The method comprises the steps of extracting multi-dimensional operation features based on a fused data sequence, and constructing a corresponding normal operation feature reference model; inputting the operation characteristics at the current moment into a normal operation characteristic reference model to obtain a characteristic deviation degree of the operation characteristics relative to the normal operation characteristic reference model, and identifying an abnormal state and an abnormal type of the starting mix package production line based on the characteristic deviation degree; when the abnormal state is identified, marking an abnormal symptom corresponding to the current abnormal state as a target abnormal symptom, and determining a corresponding fault root cause; and matching a corresponding hierarchical control strategy based on the characteristic deviation degree and the fault root cause, and controlling the starting mix package production line to execute corresponding operation adjustment or safety response operation.

The invention relates to the technical field of sorting machine control, in particular to a high-speed sorting system based on operation state perception, which comprises a load density quantification module, an operation level regulation and control module, a theoretical time sequence resolving module, a response lag compensation module and a closed loop execution feedback module. According to the invention, the real-time operation density is quantified by collecting the shielding pulse at the inlet of the sorting machine, and the operation speed gear of the box-type conveying sorting machine is dynamically matched according to the real-time operation density, so that the self-adaptive balance of energy consumption and productivity in the manufacturing process management is realized; a global coordinate system is utilized to map the real-time position of goods, a filtering algorithm is combined to predict and compensate mechanical response lag, time sequence drift caused by aging and abrasion of equipment is effectively eliminated, and operation state perception is formed based on deep mining of data such as a pulse sequence and execution feedback so as to construct a closed-loop updating mechanism. Control parameters can be corrected in real time to offset environmental interference, it is ensured that the sorting executing mechanism can be accurately triggered in different operation states, and the problem of synchronous errors caused by mechanical characteristic changes is solved.

This application relates to the field of manufacturing process management technology and discloses an AI-driven digital twin real-time scheduling method and system for mixed-flow production. The method collects real-time data streams from the production site, performs layered analysis of equipment operating parameters and material inventory status, and generates equipment fault identification results and inventory status results. Based on these results, it determines disturbances and their impact range; based on the impact range, it identifies affected production tasks and resource shortage requirements; it generates and optimizes reconfigurable paths by combining equipment allocation priority rules, material flow optimization rules, path constraints, and path conflict detection mechanisms; it issues adjustment instructions based on the prioritized execution paths and determines production recovery confirmation results based on execution feedback. This application improves the recovery efficiency and execution stability of mixed-flow manufacturing process management.

The invention provides an intelligent manufacturing process management and control method based on an AI Agent. The method comprises the steps that firstly, a task set is constructed, and the AI Agent with sensing, decision making and state maintenance capabilities is initialized; secondly, carrying out attribute modeling on various manufacturing resources, and establishing a task-resource capability function; then, a process state transition diagram and a strategy model are constructed, and task scheduling path reasoning is achieved; then, the process income is evaluated through a unified objective function, and the contribution of each Agent is evaluated; and finally, when the local income is lower than a threshold value, triggering flow reconstruction, and supporting reconstruction adjustment of the flow under resource change or emergencies, thereby realizing intelligent perception, autonomous decision and dynamic optimization control of the manufacturing flow. According to the invention, an extensible, explainable and optimizable intelligent process management infrastructure is provided for the intelligent manufacturing system, and the operation efficiency, response flexibility and collaborative intelligence level of the manufacturing system can be remarkably improved.

The application discloses an Internet of Things driven real-time production process tracing and monitoring method and system, relates to the technical field of manufacturing process management, and comprises the following steps: monitoring production lines in real time according to the Internet of Things, and obtaining various production line monitoring blocks; detecting product abnormalities of each production line according to the blocks, and obtaining a product abnormality feature set of each production line; tracing and constructing a production line abnormality tracing map from equipment, environment and other factors; and finally, managing production line abnormalities according to the three types of maps, realizing production process tracing and abnormality management. The application solves the technical problem that, in manufacturing process management, traditional methods are difficult to accurately trace product abnormalities of multiple production lines in a production workshop from equipment, environment and other factors, leading to incomplete and untimely production abnormality management, and achieves the technical effects of accurately positioning product abnormalities from equipment, environment and other factors, and improving the comprehensiveness and timeliness of production abnormality management.

The application relates to the technical field of manufacturing process management, in particular to an intelligent scheduling method and system in a media content production process, which comprises the following steps: collecting historical and current editing data in the media content production process, and preprocessing the editing data; constructing an editing time length prediction model by a statistical analysis method and introducing dynamic editing efficiency parameters, a team efficiency synchronization index and an efficiency trend index, calculating the predicted editing processing time length corresponding to each editing task and the predicted completion time of the editing task based on the preprocessed editing data; judging the scheduling constraint conditions and optimization objectives in the media content production process based on the predicted editing processing time length; and performing editing task scheduling calculation based on the scheduling constraint conditions and optimization objectives. The application realizes accurate time length prediction of editing tasks and efficient allocation of resources by fusing the static and dynamic efficiency parameters of editing personnel, team cooperation states and timing constraints.