14 results about "Lean IT" patented technology

The invention provides a lean operation method based on production scheduling optimization and equipment OEE monitoring, and relates to the technical field of lean operation, and the method comprises the steps: deploying a multi-source heterogeneous data collection interface, and carrying out the data collection, the method comprises the following steps: acquiring customer order data, material supply state data, production task priority data, process time sequence requirement data and equipment maintenance plan data, performing data preprocessing based on data acquisition, generating a semantic tag set, acquiring the semantic tag set, configuring a production demand dynamic feature set based on the obtained semantic tag set, and generating a production demand dynamic feature set; and calculating a feature influence weight. By deploying a multi-source heterogeneous data acquisition interface, customer order data, material supply state data, production task priority data, process time sequence requirement data and equipment maintenance plan data are acquired at the same time, and a semantic tag set and a production demand dynamic feature set are constructed according to a time sequence structure.

The invention relates to an intelligent digital management method for friction plate production, which belongs to the technical field of friction plate processing and comprises the following operation steps of: 1, performing intelligent production scheduling; and 2, processing the core plate, counting and summarizing, and performing formula digitization and mistake proofing according to classification requirements. And a third step of carrying out equipment data acquisition and health management on the equipment based on IIOT and SCADA equipment and data interconnection. And 4, performing appearance defect detection and performance prediction on the friction plate based on intelligent quality control of machine vision and AI. And 5, finally summarizing the number of the types of friction plates required by the order, and meanwhile, displaying the number of semi-finished products in the operation process of each production line and each device in real time. Through fusion of advanced technologies such as industrial Internet of Things, artificial intelligence, big data analysis and a manufacturing execution system, the problems of difficult production scheduling, unstable quality, information isolated island, difficult tracing and the like in traditional friction plate production are solved, so that transparency, automation, intelligence and lean production process are realized.

The invention discloses a special-shaped complex thin-wall integral precision casting heat preservation patch design method in the technical field of precision casting, which comprises the following steps of: accurately and orderly controlling a temperature field in a forming process of a high-integration special-shaped complex thin-wall integral precision casting by designing a high-adaptation asbestos heat preservation patch; sequential solidification from the special-shaped complex thin wall to the riser and efficient feeding of the riser to the special-shaped complex thin wall are achieved, the problem of looseness of the special-shaped complex thin wall is effectively solved, an accurate simulation boundary condition database of the special-shaped complex thin wall is established through temperature measurement test and simulation software reverse calculation, traditional experience trial and error and extensive process design are broken through, and the accuracy of the special-shaped complex thin wall is improved. According to the method, the forward and lean process design based on precise simulation is achieved, lean production of high-integration special-shaped complex thin-wall integral precision castings is achieved, the product quality is effectively improved, the development period is greatly shortened, and the production cost is greatly reduced.

This invention provides a control method and apparatus based on a lean manufacturing system, comprising: calculating the operational maturity value of a target lean manufacturing system to identify problems to be improved; constructing manufacturing strategic goals by comparing the target factory with business objectives based on pre-built quantitative indicators; streamlining the organizational structure based on pre-built lean processes, lean organizational structure, and lean departmental functions and responsibilities; constructing a multi-level key performance indicator system and a facilitating management meeting mechanism; and finally, constructing an implementation plan and controlling the target factory according to the implementation plan to achieve the optimization goals. This invention solves the problems of high deployment costs and poor application effects in existing technologies.

The invention relates to the technical field of non-power-cut operation, in particular to a non-power-cut operation management and control platform based on a deep learning algorithm, and the platform comprises a data collection module which is used for collecting data in real time, and the collected data comprises tool data, vehicle data, personnel data, environment data and operation flow data; a data preprocessing module; a data analysis module; an early warning module; an execution module; and a deep learning module. The platform covers a whole link of data acquisition, preprocessing, analysis, early warning and execution, and is seamlessly connected with an existing business system of a power enterprise through a data interaction interface, so that real-time sharing and collaborative linkage of data of tools, vehicles, personnel, operation processes and the like are realized, the problem of'information isolated island 'of a traditional management and control mode is thoroughly solved, and the management and control efficiency is improved. And a lean management chain of'terminal + data stream + AI decision 'is constructed.

The application provides a lean-pull-based intelligent agent task scheduling system and method, applied to the technical field of task scheduling, extracts lean-pull demand signals by introducing an intelligent enterprise system protocol adaptation layer; the intelligent agent execution scheduling center analyzes the value flow of the lean demand signals, generates task scheduling instructions; the task scheduling instructions are decomposed into executable operation step sequences by a digital employee post arrangement engine, and precise allocation and standardized execution are realized by combining role-privilege-process three-dimensional modeling; the execution state data is collected and fed back by a feedback adjustment module, and the digital employee post arrangement engine can optimize and adjust the unexecuted operation step sequences according to the real-time state, or trigger the abnormal processing flow under abnormal conditions, with the whole-link tracing mechanism. Therefore, the application has the advantages of improving the operation efficiency, resource utilization rate and lean management level of enterprises, and accelerating the iterative upgrading of the lean production mode.

The present invention provides a method and system for managing the operation, distribution, and dispatching model, including: searching for the distribution automation model to obtain the boundary devices of the dispatching model, the distribution automation model, and the marketing model; analyzing the boundary devices to obtain the overlapping device information and topological information among the distribution automation model, the marketing model, and the dispatching model; respectively processing the overlapping device information and topological information in the distribution automation model, the marketing model, and the dispatching model, and merging the distribution automation model, the marketing model, and the dispatching model to obtain the integrated operation, distribution, and dispatching model. The present invention can solve problems such as overlapping device information and blurred boundaries between operation and distribution, and between dispatching and distribution, realizes the effective management of boundary devices and the integration of the operation, distribution, and dispatching model based on the boundary devices, can provide an integrated operation, distribution, and dispatching model for the integrated power supply service command platform, and provide data support for the lean management of the distribution network operation and maintenance, the continuous improvement of power supply reliability, and the improvement of power supply service level.

The embodiment of the invention provides a method for dynamically optimizing one graph of a digital architecture based on multi-source data association mining, and belongs to the technical field of digital architecture management and optimization. The method comprises the steps of collecting multi-source architecture data and performing tagging processing; modeling based on the incidence relation of the graph database; dynamically constructing a digital architecture graph; and performing dynamic optimization based on association mining. According to the method, automatic acquisition, association mining and dynamic optimization of architecture assets are realized, and the dynamic management and control capability and lean management level of a digital architecture are improved.

The application discloses a kind of packing bag wisdom production management method and system, it is related to production management field, when AOI camera detects original defect signal, system immediately generates the defect event containing detailed production context, breaks the traditional information fragmentation situation.Subsequently, with defect timestamp as center, system initiates query to time series database, obtains PLC key parameter and MES material batch information, forms comprehensive defect context fingerprint, solves data island problem.This fingerprint is compared with digital twin baseline, identifies abnormal vector, reveals potential process deviation.Finally, abnormal vector is input into pre-trained machine learning model, combined with knowledge base, intelligently infers defect reason and provides accurate recommended action, and is displayed in real time on the screen in central control room, thereby significantly shortening root cause positioning time, realizes the intelligentization and lean of production management.

The invention relates to the technical field of project management of a power distribution operation and inspection center, and discloses a power utilization project reserve demand management method for the power distribution operation and inspection center, and the method comprises the steps: building an examination and approval process according to different project applications, inputting work order information, and storing the input work order information into a project acceptance system; a user processes an applied item through an account number, processing is carried out according to different item type distribution and corresponding users, finally a completed item application is stored in an item storage system, and a user operation process and execution and a calling record are recorded, so that the user operation record and the applied item are convenient to call and maintain, and the user experience is improved. Project reserve lean management level is improved, and digital transformation of distribution network construction is promoted.

The present application provides a lean operation method based on production scheduling optimization and equipment OEE monitoring, which relates to the field of lean operation technology, including: deploying a multi-source heterogeneous data acquisition interface, and performing data acquisition to obtain customer order data, material supply status data, production task priority data, process timing requirement data and equipment maintenance plan data, performing data preprocessing after data acquisition, and generating a semantic label set, obtaining a semantic label set, configuring a production demand dynamic feature set based on the obtained semantic label set, and calculating the feature influence weight. By deploying a multi-source heterogeneous data acquisition interface, customer order data, material supply status data, production task priority data, process timing requirement data and equipment maintenance plan data are obtained at the same time, and a semantic label set and a production demand dynamic feature set are constructed according to the time series structure.

The invention provides a lean pull-based agent task scheduling system and method, which are applied to the technical field of task scheduling, and extract a lean pull demand signal by introducing an intelligent enterprise system protocol adaptation layer; performing value flow analysis on the lean demand signal through an intelligent agent execution scheduling center, and generating a task scheduling instruction; a task scheduling instruction is decomposed into an executable operation step sequence through a digital employee post arrangement engine, and accurate distribution and standardized execution are realized by combining role-authority-process three-dimensional modeling; the execution state data is collected and fed back through the feedback adjustment module, and a full-link tracing mechanism is matched, so that the digital employee post arrangement engine can optimize and adjust an unexecuted operation step sequence according to a real-time state, or trigger an exception processing flow under an exception condition. Therefore, the method has the advantages that the operation efficiency, the resource utilization rate and the lean management level of an enterprise are improved, and iterative upgrading of a lean production mode is accelerated.

The invention provides a control method and device based on a lean production system, and the method comprises the steps: carrying out the calculation of an operation maturity value through a target lean production system, obtaining a to-be-improved problem, comparing a target factory with a business target based on a pre-constructed quantitative index, and carrying out the construction of a manufacturing strategy target; based on a pre-constructed lean process, a lean organizational structure and lean department functions and responsibilities, the organizational structure is simplified, a multi-level key performance indicator system and a promoted management conference mechanism are constructed, finally, an implementation plan is constructed, and a target factory is controlled according to the implementation plan to achieve an optimization target. The problems of high deployment cost and poor application effect in the prior art are solved.