12 results about "Mass customization" patented technology

The invention discloses a large-scale customization quality prediction method based on transfer learning. The invention designs a large-scale customization quality prediction method for solving the problems that structures among variables are difficult to excavate and the quality of products with few samples is difficult to predict due to modular production, customized assembly and inconsistent number of different product samples in large-scale customization production. The method comprises the following steps: firstly, mining a causal relationship among source domain variables by utilizing a PC causal discovery method aiming at source domain data with a richer sample size, carrying out structure migration on a part of fixed structures from a source domain to a target domain by utilizing mechanism knowledge in order to obtain the causal relationship among target domain variables, and carrying out target domain few-sample amplification through a generative adversarial network; a causal relationship is complemented in a target domain after variable amplification through a PC causal discovery method, a target domain variable causal graph structure is obtained, then a heterogeneous causal graph attention network is utilized to carry out source domain and target domain model training, and target domain quality prediction is carried out through an MAML transfer learning method with MMD distance weighting. Aiming at the characteristics of large-scale customized production of customized products, the transfer learning-based quality prediction method suitable for large-scale customized production is formed.

The invention provides a large-scale customized production abnormity regulation and control method based on a knowledge graph in order to solve the technical problems that an existing production abnormity regulation and control technology mainly focuses on recognition and diagnosis of a single abnormity type, only local accurate analysis can be achieved, and large-scale customized production multi-source abnormity cooperative regulation and control cannot be achieved. By constructing the knowledge graph in the large-scale customized production scene fused with multi-source data and combining the established anomaly classification system and the abnormal event influence quantitative grading model, the anomaly is accurately recognized and graded, and different grades of abnormal events in large-scale customized production can be solved by effectively utilizing experience knowledge.

This invention discloses a system and method for automatically customizing shoe lasts, including modules and methods for automatic model alignment, automatic extraction of foot shape feature parameters, shoe last size adjustment, foot deformation simulation calculation, and automatic generation calculation of customized shoe lasts. It is used to automatically adjust a standard shoe last model and the user's foot shape model into a customized shoe last model that conforms to the user's foot shape when wearing shoes. The system and method for automatically customizing shoe lasts of this invention require almost no human intervention in the customization design process. After inputting the user's foot shape model, a customized shoe last model is automatically generated, greatly improving design efficiency and realizing design automation. This provides an efficient and low-cost implementation model for large-scale customized shoe businesses. Due to the fully digital design process and input / output, it can easily interface with front-end data acquisition modes and back-end digital manufacturing terminals.

This invention discloses a closed-loop generation and cutting system and method for dynamic patterns of clothing for personalized sports. The system includes: an integrated dynamic capture unit for synchronously collecting visual point cloud data and mechanical data during human movement to construct a 4D data tensor; a dedicated analysis host for performing dynamic skin strain field mapping, topology optimization of dividing lines, and dynamic ease compensation calculation based on fabric physical property parameters and the 4D data tensor to generate an optimized pattern; and a driver and execution terminal directly connected to the host to directly convert the pattern data into cutting instructions to complete the cutting. This invention, through a "sports energy consumption-skin stretching-fabric mechanics" model, generates clothing that significantly reduces structural resistance during movement. Experiments show that in large-amplitude movements such as golf swings, it can reduce motion energy loss by approximately 5%-10%. This solves the industry problem of balancing "personalized patterns" and "automated production" in mass customization.

The invention provides an integrated micro-channel methanol reactor and a preparation method of a reforming chamber system of the integrated micro-channel methanol reactor. The methanol reactor comprises an upper shell, a lower shell and an inner shell, the inner shell is arranged in the lower shell; the upper extension part of the lower shell, the upper extension part of the inner shell and the lower extension part of the upper shell are connected through bolts, and an isolation panel is arranged between the upper shell and the inner shell; a reactant inlet is also formed in the lower shell; a diffusion buffer area and a reforming chamber system are arranged in the inner shell; a gas outlet buffer cavity and a reformate outlet are formed in the upper shell; the reactant inlet, the diffusion buffer area, the reforming chamber system, the gas outlet buffer cavity and the reforming product outlet are sequentially connected to form a reforming reaction channel. The reforming chamber system with the reaction microchannel is prepared by adopting a 3D printing technology, so that the conversion efficiency of formaldehyde is improved, and the reforming chamber system is suitable for large-scale customized production.

The application provides a BOM accuracy verification method and system and electronic equipment. The BOM accuracy verification method comprises the following steps: obtaining a configuration table of a product and a configurable BOM table based on a modular structure of the product; and verifying the accuracy of each module of the product based on configuration constraint conditions and configuration codes of configuration groups in the configuration table and preset configuration codes of each module of the product in the configurable BOM table, to obtain a verification result of the configurable BOM table representing the accuracy of the configurable BOM table. In the case that the number of product variants is indefinite, the verification of the configurable BOM table can be implemented without defining all product variants in advance, thereby solving the problem that the BOM accuracy verification of large-scale customized products is difficult to implement in the prior art.

Performance-based designs follow building codes that specify the desired goals of a building, allowing more flexibility and innovation. Although the current modular and manufactured methods offer fewer manufacturing steps and reduced waste compared to traditional construction, production of modular components requires use of multiple materials and machines, and the processes are not highly precise or repeatable. The present disclosure incorporates performance- based designs and advanced manufacturing methods to fabricate construction components and / or sub-components that are intended to be utilized in building structures. The components and / or sub-components are manufactured by single machines or work cells in controlled environments and are subsequently transported to the site where minimal work is involved for assembly. The performance-based designs are applied in the initial design, throughout the life, and at the end-of- life reuse plan of the components. The current disclosure enables mass customization, precision and repeatability, and use of recyclable and biobased materials.

The application discloses a processing information analysis and abnormality processing method and system for customized furniture production, relates to the technical field of furniture manufacturing, and realizes complete traceability, improves abnormality identification precision and response efficiency, reduces production failure and quality risk, and is suitable for intelligent management and control of large-scale customized furniture production by collecting order information, generating a unique traceability identifier, collecting and standardizing processing of full-link data such as order splitting, raw materials, processing, assembly, detection, logistics and installation, and constructing a complete traceability link; based on an improved LSTM network and the introduction of an attention mechanism, an abnormality early warning model is established to realize online monitoring and abnormality discrimination of production data, hierarchical early warning and rapid disposal. The system comprises an order information collection module, a full-process data collection module, a data processing module, an abnormality early warning module, a traceability disposal module, a database and a terminal interaction module.

A method of providing a custom formulated product, including skincare products, is described. A user may enter user skincare data and a user skin profile may be generated from this data. The user skin profile may be mapped to skin matrix factors of a user skin profile matrix to derive a skin profile identifier. The skin profile identifier may be used to select ingredients to manufacture a custom formulated skin product, specific to the skin issues that the user identified. The custom formulated product can then be distributed directly to the user.

The invention discloses an information acquisition and management method for a large-scale customized furniture manufacturing workshop, and relates to the technical field of furniture production, and the method comprises the following steps: S1, order acquisition: receiving user customized order data through an enterprise resource planning system, carrying out the field analysis, and then extracting the needed processing procedures and the execution sequence thereof; s2, a dynamic task model is generated, and based on the needed machining procedures and the execution sequence of the machining procedures, the task models are sequentially pushed to execution terminals of corresponding stations through a task model generation module according to the sequence of the machining procedures; by analyzing a customized order field and combining a preset process template and a rule engine, automatic construction of a personalized task model is realized, and the method adapts to multi-variety and small-batch customization scenes; abnormality is recognized through multi-source real-time data collection and model comparison, specifically, machining state data of all stations are collected, abnormality recognition is carried out in combination with a task model, multiple types of abnormalities such as process timeout, sequence hopping, equipment offline and data illegality are included, and closed-loop quality and process monitoring is achieved.

Provided is a mass customization system. The mass customization system includes a raw material stereoscopic warehouse module, an accessory assembly module, and a customization and detection module. The raw material stereoscopic warehouse module is configured to store a plurality of components for a plurality of types of products and pick out components of a target product according to order information. The accessory assembly module is configured to receive and assemble the components of the target product to obtain the target product. The customization and detection module is configured to perform product customization on the target product according to customization information carried in the order information.

The application discloses a production scheduling and distribution collaborative optimization method and device under mass customization, and relates to the technical field of production scheduling and distribution collaborative optimization. The method comprises the following steps: obtaining production data to be optimized; inputting the production data to be optimized into a constructed production scheduling and distribution collaborative optimization model; adopting a multi-objective evolutionary algorithm IMOEA / COD based on collaboration and decomposition to solve the production scheduling and distribution collaborative optimization model, and obtaining an optimized production scheduling and distribution scheme. The application can solve how to collaboratively control two stages of production and distribution under mass customization, and formulate a reasonable production scheduling and distribution scheme, so as to realize the reduction of maximum completion time, distribution cost and lead time / tardiness cost. The application considers the mutual influence of the two stages of production and distribution, can fully utilize existing resources, and effectively solves the production scheduling and distribution collaborative optimization problem under mass customization.